1
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Chen J, Yang X, Wu H. A Multibranch Neural Network for Drug-Target Affinity Prediction Using Similarity Information. ACS OMEGA 2024; 9:35978-35989. [PMID: 39184467 PMCID: PMC11339836 DOI: 10.1021/acsomega.4c05607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 08/03/2024] [Accepted: 08/06/2024] [Indexed: 08/27/2024]
Abstract
Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. In recent years, graph structure-based deep learning models have garnered significant attention in this field. However, these models typically handle drug or target protein in isolation and only extract the molecular structure information on the drug or protein itself. To address this limitation, existing network-based models represent drug-target interactions or affinities as a knowledge graph to capture the interaction information. In this study, we propose a novel solution. Specifically, we introduce drug similarity information and protein similarity information into the field of DTA prediction. Moreover, we propose a network framework that autonomously extracts similarity information, avoiding reliance on knowledge graphs. Based on this framework, we design a multibranch neural network called GASI-DTA. This network integrates similarity information, sequence information, and molecular structure information. Comprehensive experimental results conducted on two benchmark data sets and three cold-start scenarios demonstrate that our model outperforms state-of-the-art graph structure-based methods in nearly all metrics. Furthermore, it exhibits significant advantages over existing network-based models, outperforming the best of them in the majority of metrics. Our study's code and data are openly accessible at http://github.com/XiaoLin-Yang-S/GASI-DTA.
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Affiliation(s)
- Jing Chen
- School
of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
- Jiangsu
Provincial Engineering Laboratory of Pattern Recognition and Computing
Intelligence, Jiangnan University, Wuxi 214122, China
| | - Xiaolin Yang
- School
of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Haoyu Wu
- School
of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
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2
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Xu Z, Li W, Dong X, Chen Y, Zhang D, Wang J, Zhou L, He G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin Chim Acta 2024; 559:119686. [PMID: 38663471 DOI: 10.1016/j.cca.2024.119686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/03/2024]
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.
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Affiliation(s)
- Zishan Xu
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Wei Li
- School of Forensic Medicine, Xinxiang Medical University, Xinxiang 453000, China
| | - Xiangyang Dong
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Yingying Chen
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453000, China
| | - Dan Zhang
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Jingnan Wang
- Xinxiang Medical University SanQuan Medical College, Xinxiang 453003, China
| | - Lin Zhou
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Guoyang He
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China.
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3
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Qi H, Yu T, Yu W, Liu C. Drug-target affinity prediction with extended graph learning-convolutional networks. BMC Bioinformatics 2024; 25:75. [PMID: 38365583 PMCID: PMC10874073 DOI: 10.1186/s12859-024-05698-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery. RESULTS To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy. CONCLUSIONS The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.
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Affiliation(s)
- Haiou Qi
- Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Ting Yu
- Operating Room Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Wenwen Yu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chenxi Liu
- School of Medicine and Health Management, Tongji Medical School, Huazhong University of Science and Technology, Wuhan, 430030, China
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4
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Yi JC, Yang ZY, Zhao WT, Yang ZJ, Zhang XC, Wu CK, Lu AP, Cao DS. ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization. Brief Bioinform 2024; 25:bbae008. [PMID: 38385872 PMCID: PMC10883642 DOI: 10.1093/bib/bbae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 02/23/2024] Open
Abstract
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.
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Affiliation(s)
- Jia-Cai Yi
- School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Wen-Tao Zhao
- School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Xiao-Chen Zhang
- School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China
| | - Cheng-Kun Wu
- State Key Laboratory of High-Performance Computing, Changsha 410073, Hunan, PR China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
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5
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Zhang L, Ouyang C, Liu Y, Liao Y, Gao Z. Multimodal contrastive representation learning for drug-target binding affinity prediction. Methods 2023; 220:126-133. [PMID: 37952703 DOI: 10.1016/j.ymeth.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023] Open
Abstract
In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era of big data. Recent years we have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task. However, these models only considered a single modality of drug and target information, and some valuable information was not fully utilized. In fact, the information of different modalities of drug and target can complement each other, and more valuable information can be obtained by fusing the information of different modalities. In this paper, we introduce a multimodal information fusion model for DTA prediction that is called FMDTA, which fully considers drug/target information in both string and graph modalities and balances the feature representations of different modalities by a contrastive learning approach. In addition, we exploited the alignment information of drug atoms and target residues to capture the positional information of string patterns, which can extract more useful feature information in SMILES and target sequences. Experimental results on two benchmark datasets show that FMDTA outperforms the state-of-the-art model, demonstrating the feasibility and excellent feature capture capability of FMDTA. The code of FMDTA and the data are available at: https://github.com/bestdoubleLin/FMDTA.
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Affiliation(s)
- Linlin Zhang
- School of Computer, University of South China, Hengyang, China
| | - Chunping Ouyang
- School of Computer, University of South China, Hengyang, China.
| | - Yongbin Liu
- School of Computer, University of South China, Hengyang, China
| | - Yiming Liao
- The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Zheng Gao
- Department of Information and Library Science, Indiana University Bloomington, Bloomington, United States
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6
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Zhao Q, Chen Y, Huang W, Zhou H, Zhang W. Drug-microbiota interactions: an emerging priority for precision medicine. Signal Transduct Target Ther 2023; 8:386. [PMID: 37806986 PMCID: PMC10560686 DOI: 10.1038/s41392-023-01619-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 07/20/2023] [Accepted: 08/24/2023] [Indexed: 10/10/2023] Open
Abstract
Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.
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Affiliation(s)
- Qing Zhao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Yao Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Weihua Huang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China.
- The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, PR China.
- The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, PR China.
- Central Laboratory of Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Changsha, 410013, PR China.
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7
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Zhang Y, Hu Y, Han N, Yang A, Liu X, Cai H. A survey of drug-target interaction and affinity prediction methods via graph neural networks. Comput Biol Med 2023; 163:107136. [PMID: 37329615 DOI: 10.1016/j.compbiomed.2023.107136] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/29/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play important roles in the field of drug discovery. However, biological experiment-based methods are time-consuming and expensive. Recently, computational-based approaches have accelerated the process of drug-target relationship prediction. Drug and target features are represented in structure-based, sequence-based, and graph-based ways. Although some achievements have been made regarding structure-based representations and sequence-based representations, the acquired feature information is not sufficiently rich. Molecular graph-based representations are some of the more popular approaches, and they have also generated a great deal of interest. In this article, we provide an overview of the DTI prediction and DTA prediction tasks based on graph neural networks (GNNs). We briefly discuss the molecular graphs of drugs, the primary sequences of target proteins, and the graph reSLBpresentations of target proteins. Meanwhile, we conducted experiments on various fundamental datasets to substantiate the plausibility of DTI and DTA utilizing graph neural networks.
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Affiliation(s)
- Yue Zhang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
| | - Yuqing Hu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Na Han
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Aqing Yang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Xiaoyong Liu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
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8
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Chen C, Hernandez JC, Uthaya Kumar DB, Machida T, Tahara SM, El‐Khoueiry A, Li M, Punj V, Swaminathan SK, Kirtane A, Chen Y, Panyam J, Machida K. Profiling of Circulating Tumor Cells for Screening of Selective Inhibitors of Tumor-Initiating Stem-Like Cells. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206812. [PMID: 36949364 PMCID: PMC10190641 DOI: 10.1002/advs.202206812] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/20/2023] [Indexed: 05/18/2023]
Abstract
A critical barrier to effective cancer therapy is the improvement of drug selectivity, toxicity, and reduced recurrence of tumors expanded from tumor-initiating stem-like cells (TICs). The aim is to identify circulating tumor cell (CTC)-biomarkers and to identify an effective combination of TIC-specific, repurposed federal drug administration (FDA)-approved drugs. Three different types of high-throughput screens targeting the TIC population are employed: these include a CD133 (+) cell viability screen, a NANOG expression screen, and a drug combination screen. When combined in a refined secondary screening approach that targets Nanog expression with the same FDA-approved drug library, histone deacetylase (HDAC) inhibitor(s) combined with all-trans retinoic acid (ATRA) demonstrate the highest efficacy for inhibition of TIC growth in vitro and in vivo. Addition of immune checkpoint inhibitor further decreases recurrence and extends PDX mouse survival. RNA-seq analysis of TICs reveals that combined drug treatment reduces many Toll-like receptors (TLR) and stemness genes through repression of the lncRNA MIR22HG. This downregulation induces PTEN and TET2, leading to loss of the self-renewal property of TICs. Thus, CTC biomarker analysis would predict the prognosis and therapy response to this drug combination. In general, biomarker-guided stratification of HCC patients and TIC-targeted therapy should eradicate TICs to extend HCC patient survival.
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Affiliation(s)
- Chia‐Lin Chen
- Departments of Molecular Microbiology and ImmunologyUniversity of Southern CaliforniaLos AngelesCA90033USA
- Present address:
Department of Life Sciences & Institute of Genome SciencesNational Yang Ming Chiao Tung University110TaipeiTaiwan
| | - Juan Carlos Hernandez
- Departments of Molecular Microbiology and ImmunologyUniversity of Southern CaliforniaLos AngelesCA90033USA
- California State UniversityChannel IslandsCamarilloCAUSA
| | - Dinesh Babu Uthaya Kumar
- Departments of Molecular Microbiology and ImmunologyUniversity of Southern CaliforniaLos AngelesCA90033USA
| | - Tatsuya Machida
- Departments of Molecular Microbiology and ImmunologyUniversity of Southern CaliforniaLos AngelesCA90033USA
| | - Stanley M. Tahara
- Departments of Molecular Microbiology and ImmunologyUniversity of Southern CaliforniaLos AngelesCA90033USA
| | - Anthony El‐Khoueiry
- Norris Comprehensive Cancer CenterUniversity of Southern CaliforniaKeck School of MedicineLos AngelesCA90033USA
| | - Meng Li
- Norris Medical Library2003 Zonal AveLos AngelesCA90089USA
| | - Vasu Punj
- Department of MedicineUniversity of Southern California Keck School of Medicine and Norris Comprehensive Cancer CenterLos AngelesCA90089USA
| | | | - Ameya Kirtane
- Department of PharmaceuticsUniversity of MinnesotaMinneapolisMN55455USA
| | - Yibu Chen
- Norris Medical Library2003 Zonal AveLos AngelesCA90089USA
| | - Jayanth Panyam
- Department of PharmaceuticsUniversity of MinnesotaMinneapolisMN55455USA
| | - Keigo Machida
- Departments of Molecular Microbiology and ImmunologyUniversity of Southern CaliforniaLos AngelesCA90033USA
- Southern California Research Center for ALPD and CirrhosisLos AngelesCA90033USA
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9
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Voitsitskyi T, Stratiichuk R, Koleiev I, Popryho L, Ostrovsky Z, Henitsoi P, Khropachov I, Vozniak V, Zhytar R, Nechepurenko D, Yesylevskyy S, Nafiiev A, Starosyla S. 3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs. RSC Adv 2023; 13:10261-10272. [PMID: 37006369 PMCID: PMC10065141 DOI: 10.1039/d3ra00281k] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/26/2023] [Indexed: 04/03/2023] Open
Abstract
Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement.
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Affiliation(s)
- Taras Voitsitskyi
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine Nauky Ave. 46 03038 Kyiv Ukraine
| | - Roman Stratiichuk
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Biophysics and Medical Informatics, Educational and Scientific Centre "Institute of Biology and Medicine", Taras Shevchenko National University of Kyiv 64 Volodymyrska Str. 01601 Kyiv Ukraine
| | - Ihor Koleiev
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
| | | | | | | | | | | | - Roman Zhytar
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
| | | | - Semen Yesylevskyy
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences CZ-166 10 Prague 6 Czech Republic
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine Nauky Ave. 46 03038 Kyiv Ukraine
| | - Alan Nafiiev
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
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10
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Carss KJ, Deaton AM, Del Rio-Espinola A, Diogo D, Fielden M, Kulkarni DA, Moggs J, Newham P, Nelson MR, Sistare FD, Ward LD, Yuan J. Using human genetics to improve safety assessment of therapeutics. Nat Rev Drug Discov 2023; 22:145-162. [PMID: 36261593 DOI: 10.1038/s41573-022-00561-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 02/07/2023]
Abstract
Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.
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Affiliation(s)
| | - Aimee M Deaton
- Amgen, Cambridge, MA, USA.,Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Alberto Del Rio-Espinola
- Novartis Institutes for BioMedical Research, Basel, Switzerland.,GentiBio Inc., Cambridge, MA, USA
| | | | - Mark Fielden
- Amgen, Thousand Oaks, MA, USA.,Kate Therapeutics, San Diego, CA, USA
| | | | - Jonathan Moggs
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | - Frank D Sistare
- Merck & Co., West Point, PA, USA.,315 Meadowmont Ln, Chapel Hill, NC, USA
| | - Lucas D Ward
- Amgen, Cambridge, MA, USA. .,Alnylam Pharmaceuticals, Cambridge, MA, USA.
| | - Jing Yuan
- Amgen, Cambridge, MA, USA.,Pfizer, Cambridge, MA, USA
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11
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Spellicy SE, Hess DC. Recycled Translation: Repurposing Drugs for Stroke. Transl Stroke Res 2022; 13:866-880. [PMID: 35218497 PMCID: PMC9844207 DOI: 10.1007/s12975-022-01000-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/16/2022] [Accepted: 02/19/2022] [Indexed: 01/19/2023]
Abstract
Stroke, which continues to be a leading cause of death and long-term disability worldwide, has often been described as a clinical graveyard. While multiple small molecule therapeutics have undergone clinical trials in stroke, currently only one Food and Drug Administration (FDA)-approved medication exists for the treatment of stroke, the biological, recombinant tissue plasminogen activator (rt-PA). Repurposing of therapeutics which have previously gained FDA approval for alternative indications serves as a prospective option for stroke therapeutic translation. In contrast to de novo drug development, repurposing strategies have patient-centered and economic advantages. These include increased safety, increased chance of approval, decreased time to approval, and decreased capital investment. Presently, 37 active stroke clinical trials utilize repurposed therapeutics with various initial indications and dosing paradigms. The currently studied repurposed therapeutics fall into six mechanistic categories: (1) anticoagulation; (2) vasculature integrity, response, or red blood cell (RBC) alterations; (3) immune system regulation; (4) neurotransmission; and (5) neuroprotection. Directed hypothesis-driven computational investigation utilizing drug databases, in silico drug-protein interaction modeling, genomic data, and consensus methodology can determine if the current mechanistic repurposing categories have the highest chance of translational success or if other mechanistic avenues should be explored. With this increased focus on repurposed therapeutic strategies over de novo strategies, evolution and optimization of regulatory protections are needed to incentivize innovators and investigators.
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Affiliation(s)
- Samantha E. Spellicy
- M.D./Ph.D. Program, Office of Academic Affairs, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - David C. Hess
- Dean’s Office, Medical College of Georgia at Augusta University, Augusta, GA, USA
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12
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Yan X, Liu Y. Graph-sequence attention and transformer for predicting drug-target affinity. RSC Adv 2022; 12:29525-29534. [PMID: 36320763 PMCID: PMC9562047 DOI: 10.1039/d2ra05566j] [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: 09/04/2022] [Accepted: 10/04/2022] [Indexed: 11/30/2022] Open
Abstract
Drug-target binding affinity (DTA) prediction has drawn increasing interest due to its substantial position in the drug discovery process. The development of new drugs is costly, time-consuming, and often accompanied by safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. Therefore, it is of great significance to develop effective computational methods to predict DTAs. The attention mechanisms allow the computational method to focus on the most relevant parts of the input and have been proven to be useful for various tasks. In this study, we proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. For the representation of drugs, we use Bi-directional Gated Recurrent Units (BiGRU) to extract the SMILES representation from SMILES sequences, and graph neural networks to extract the graph representation of the molecular graphs. Then we utilize an attention mechanism to fuse the two representations of the drug. For the target/protein, we utilized an efficient transformer to learn the representation of the protein, which can capture the long-distance relationships in the sequence of amino acids. We conduct extensive experiments to compare our model with state-of-the-art models. Experimental results show that our model outperforms the current state-of-the-art methods on two independent datasets.
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Affiliation(s)
- Xiangfeng Yan
- School of Computer Science and Technology, Heilongjiang UniversityHarbinChina
| | - Yong Liu
- School of Computer Science and Technology, Heilongjiang UniversityHarbinChina
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13
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Kitsiranuwat S, Suratanee A, Plaimas K. Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction. Sci Prog 2022; 105:368504221109215. [PMID: 35801312 PMCID: PMC10358641 DOI: 10.1177/00368504221109215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Identifying new therapeutic indications for existing drugs is a major challenge in drug repositioning. Most computational drug repositioning methods focus on known targets. Analyzing multiple aspects of various protein associations provides an opportunity to discover underlying drug-associated proteins that can be used to improve the performance of the drug repositioning approaches. In this study, machine learning models were developed based on the similarities of diversified biological features, including protein interaction, topological network, sequence alignment, and biological function to predict protein pairs associating with the same drugs. The crucial set of features was identified, and the high performances of protein pair predictions were achieved with an area under the curve (AUC) value of more than 93%. Based on drug chemical structures, the drug similarity levels of the promising protein pairs were used to quantify the inferred drug-associated proteins. Furthermore, these proteins were employed to establish an augmented drug-protein matrix to enhance the efficiency of three existing drug repositioning techniques: a similarity constrained matrix factorization for the drug-disease associations (SCMFDD), an ensemble meta-paths and singular value decomposition (EMP-SVD) model, and a topology similarity and singular value decomposition (TS-SVD) technique. The results showed that the augmented matrix helped to improve the performance up to 4% more in comparison to the original matrix for SCMFDD and EMP-SVD, and about 1% more for TS-SVD. In summary, inferring new protein pairs related to the same drugs increase the opportunity to reveal missing drug-associated proteins that are important for drug development via the drug repositioning technique.
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Affiliation(s)
- Satanat Kitsiranuwat
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Advanced Virtual and Intelligent Computing (AVIC) center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand
- Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand
| | - Kitiporn Plaimas
- Advanced Virtual and Intelligent Computing (AVIC) center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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14
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Farnoud A, Ohnmacht AJ, Meinel M, Menden MP. Can artificial intelligence accelerate preclinical drug discovery and precision medicine? Expert Opin Drug Discov 2022; 17:661-665. [PMID: 35708267 DOI: 10.1080/17460441.2022.2090540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Precision medicine leverages molecular biomarkers for selecting optimal treatment strategies. Accordingly, stratifying patients into responder and non-responder has strongly accelerated drug discovery and drug approvals in the last two decades. Recently, the applications of artificial intelligence (AI) in healthcare have been promoting these processes, and continue to improve patient care through systematically analysing large-scale molecular data and electronic health records. In particular, preclinical pharmacogenomics data empowered AI to unfold its full potential.Areas covered: Here, we discuss the opportunities of AI in pharmacogenomics, drug discovery and precision medicine. In particular, we shed some light on the advancements in computational biomedicine from statistical, machine learning (ML) to complex deep learning (DL) models.Expert opinion: AI has already strongly impacted drug discovery, and will continue to revolutionise academic research and the pharmaceutical industry. Its algorithms aid the identification of novel treatment options through molecular signatures and thus pave the way for the next generation of precision medicine.
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Affiliation(s)
- Ali Farnoud
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
| | - Alexander J Ohnmacht
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Martin Meinel
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.,Department of Dermatology and Allergy, Technical University Munich, Munich, Germany
| | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Munich, Germany
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15
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Translational advances of melanocortin drugs: Integrating biology, chemistry and genetics. Semin Immunol 2022; 59:101603. [PMID: 35341670 DOI: 10.1016/j.smim.2022.101603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/10/2022] [Accepted: 03/15/2022] [Indexed: 01/15/2023]
Abstract
Melanocortin receptors have emerged as important targets with a very unusual versatility, as their widespread distribution on multiple tissues (e.g. skin, adrenal glands, brain, immune cells, exocrine glands) together with the variety of physiological processes they control (pigmentation, cortisol release, satiety mechanism, inflammation, secretions), place this family of receptors as genuine therapeutic targets for many disorders. This review focuses in the journey of the development of melanocortin receptors as therapeutic targets from the discovery of their existence in the early 1990 s to the approval of the first few drugs of this class. Two major areas of development characterise the current state of melanocortin drug development: their role in obesity, recently culminated with the approval of setmelanotide, and their potential for the treatment of chronic inflammatory and autoimmune diseases like rheumatoid arthritis, multiple sclerosis or fibrosis. The pro-resolving nature of these drugs offers the advantage of acting by mimicking the way our body naturally resolves inflammation, expecting fewer side effects and a more balanced (i.e. non-immunosuppressive) response from them. Here we also review the approaches followed for the design and development of novel compounds, the importance of the GPCR nature of these receptors in the process of drug development, therapeutic value, current challenges and successes, and the potential for the implementation of precision medicine approaches through the incorporation of genetics advances.
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16
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Shan Y, Wang F, Wei Z, Lu Y. Synthetic lethality theory approaches to effective substance discovery and functional mechanisms elucidation of anti-cancer phytomedicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2021; 91:153718. [PMID: 34531099 DOI: 10.1016/j.phymed.2021.153718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/04/2021] [Accepted: 08/15/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Longstanding, successful use of combinations of phytopharmaceuticals in traditional Chinese medicine (TCM) has caught the attention of several pharmacologists to natural medicines. However, the development and popularisation of TCM is mainly limited because of the unavailability of reports clarifying the mechanisms of action and pharmacologically active ingredients in such formulations. Previous studies on natural medicines have mostly focused on their dominant components using forward pharmacology which often neglects trace components. It is necessary to assess the pharmacological and therapeutic superiority of many such trace components in comparison with single constituents. PURPOSE In this study, we aimed to propose a new pharmacological research strategy for TCM. In particular, we presented the possibility that the effective mechanism of action of trace components of TCM is based on synthetic lethality. We sincerely hope to explore this theory further. METHOD We obtained retrieve published research information related to synthetic lethality, phytochemicals and Chinese medicine from PubMed and Google scholar. Based on the inclusion criteria, 71 studies were selected and discussed in this review. RESULTS As an interaction among genes, synthetic lethality can amplify co-regulatory biological effects exponentially. Synthetic strategies have been successfully applied for research and development of anti-tumour agents, including poly ADP-ribose polymerase inhibitors and clinical combination of chemotherapeutic agents for efficacy enhancement and toxicity reduction. TCM drugs contain several secondary metabolites to combat environmental stresses, providing a multi-component basis for corresponding synergistic targets. Therefore, we aimed to study whether this method could be used to identify active components present in trace amounts in TCM drugs. Based on a reverse concept of target-component-effect and identified synergistic targets, we explored the mechanisms of action of weakly active components present in trace amounts in TCM drugs to assess combinations of potential synergistic components. CONCLUSION This pattern of synthetic lethality not only elucidated the mechanisms of action of TCM drugs from a new perspective but also inspired future studies on discovering naturally occurring active components.
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Affiliation(s)
- Yunlong Shan
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Fang Wang
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China
| | - Zhonghong Wei
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Yin Lu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
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17
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Chen Y, Zhang L. How much can deep learning improve prediction of the responses to drugs in cancer cell lines? Brief Bioinform 2021; 23:6370847. [PMID: 34529029 DOI: 10.1093/bib/bbab378] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 12/24/2022] Open
Abstract
The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction. We summarize and examine state-of-the-art deep learning methods that have been published recently. Although significant progresses have been made in deep learning approach in drug response prediction, deep learning methods show their weakness for predicting the response of a drug that does not appear in the training dataset. In particular, all the five evaluated deep learning methods performed worst than the similarity-regularized matrix factorization (SRMF) method in our drug blind test. We outline the challenges in applying deep learning approach to drug response prediction and suggest unique opportunities for deep learning integrated with established bioinformatics analyses to overcome some of these challenges.
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Affiliation(s)
- Yurui Chen
- Department of Mathematics and Computational Biology Programme, National University of Singapore, 119076, Singapore
| | - Louxin Zhang
- Department of Mathematics and Computational Biology Programme, National University of Singapore, 119076, Singapore
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18
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Tukker AM, Westerink RHS. Novel test strategies for in vitro seizure liability assessment. Expert Opin Drug Metab Toxicol 2021; 17:923-936. [PMID: 33595380 PMCID: PMC8367052 DOI: 10.1080/17425255.2021.1876026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/11/2021] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The increasing incidence of mental illnesses and neurodegenerative diseases results in a high demand for drugs targeting the central nervous system (CNS). These drugs easily reach the CNS, have a high affinity for CNS targets, and are prone to cause seizures as an adverse drug reaction. Current seizure liability assessment heavily depends on in vivo or ex vivo animal models and is therefore ethically debated, labor intensive, expensive, and not always predictive for human risk. AREAS COVERED The demand for CNS drugs urges the development of alternative safety assessment strategies. Yet, the complexity of the CNS hampers reliable detection of compound-induced seizures. This review provides an overview of the requirements of in vitro seizure liability assays and highlights recent advances, including micro-electrode array (MEA) recordings using rodent and human cell models. EXPERT OPINION Successful and cost-effective replacement of in vivo and ex vivo models for seizure liability screening can reduce animal use for drug development, while increasing the predictive value of the assays, particularly if human cell models are used. However, these novel test strategies require further validation and standardization as well as additional refinements to better mimic the human in vivo situation and increase their predictive value.
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Affiliation(s)
- Anke M. Tukker
- School of Health Sciences, Purdue University, Hall for Discovery and Learning Research (DLR 339), INUSA
| | - Remco H. S. Westerink
- Neurotoxicology Research Group, Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, TD Utrecht, The Netherlands
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19
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Serotonergic receptor gene polymorphism and response to selective serotonin reuptake inhibitors in ethnic Malay patients with first episode of major depressive disorder. THE PHARMACOGENOMICS JOURNAL 2021; 21:498-509. [PMID: 33731884 DOI: 10.1038/s41397-021-00228-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 01/29/2021] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
The polymorphisms of the 5HTR1A and 5HTR2A receptor genes (rs6295C/G and rs6311G/A) have been evaluated for association with SSRI treatment outcome in various populations with different results. The present study was carried out to determine the association between genotypes of HTR1A-rs6295 and HTR2A-rs6311 with SSRI treatment outcome among the ethnic Malay patients diagnosed with first-episode major depressive disorder (MDD). The patients were recruited from four tertiary hospitals in the Klang Valley region of Malaysia. Predefined efficacy phenotypes based on 25% (partial early response) and 50% (clinical efficacy response) reduction in Montgomery Asberg Depression Rating Scale-self Rated score (MADRS-S) were adopted for assessment of treatment efficacy in this study. Self-reporting for adverse effects (AE) was documented using the Patient Rated Inventory of Side Effect (PRISE) after treatment with SSRI for up to 6 weeks. Adjusted binary logistic regression between genotypes of the polymorphism obtained using sequencing technique with the treatment outcome phenotypes was performed. The 142 patients recruited were made up of 96 females (67.6%) and 46 males (32.4%). Clinical efficacy and Partial early response phenotypes were not significantly associated with genotypes of HTR1A and HTR2A polymorphism. The GG genotype of HTR2A polymorphism has decreased odds for dizziness (CNS) and increased odds for poor concentration. The GA genotype increases the odd for excessive sweating, diarrhoea, constipation and blurred vision. The CC genotype of HTR1A-rs6295 decreases the odd for nausea/vomiting and increases the odd for anxiety. Thus, some genotypes of HTR1A and HTR2A polymorphism were associated with SSRI treatment outcomes in ethnic Malay MDD patients.
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20
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Saunders AM, Burns DK, Gottschalk WK. Reassessment of Pioglitazone for Alzheimer's Disease. Front Neurosci 2021; 15:666958. [PMID: 34220427 PMCID: PMC8243371 DOI: 10.3389/fnins.2021.666958] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/18/2021] [Indexed: 01/01/2023] Open
Abstract
Alzheimer's disease is a quintessential 'unmet medical need', accounting for ∼65% of progressive cognitive impairment among the elderly, and 700,000 deaths in the United States in 2020. In 2019, the cost of caring for Alzheimer's sufferers was $244B, not including the emotional and physical toll on caregivers. In spite of this dismal reality, no treatments are available that reduce the risk of developing AD or that offer prolonged mitiagation of its most devestating symptoms. This review summarizes key aspects of the biology and genetics of Alzheimer's disease, and we describe how pioglitazone improves many of the patholophysiological determinants of AD. We also summarize the results of pre-clinical experiments, longitudinal observational studies, and clinical trials. The results of animal testing suggest that pioglitazone can be corrective as well as protective, and that its efficacy is enhanced in a time- and dose-dependent manner, but the dose-effect relations are not monotonic or sigmoid. Longitudinal cohort studies suggests that it delays the onset of dementia in individuals with pre-existing type 2 diabetes mellitus, which small scale, unblinded pilot studies seem to confirm. However, the results of placebo-controlled, blinded clinical trials have not borne this out, and we discuss possible explanations for these discrepancies.
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Affiliation(s)
- Ann M. Saunders
- Zinfandel Pharmaceuticals, Inc., Chapel Hill, NC, United States
| | - Daniel K. Burns
- Zinfandel Pharmaceuticals, Inc., Chapel Hill, NC, United States
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21
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Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 2021; 37:1140-1147. [PMID: 33119053 DOI: 10.1093/bioinformatics/btaa921] [Citation(s) in RCA: 284] [Impact Index Per Article: 94.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/01/2020] [Accepted: 10/15/2020] [Indexed: 12/21/2022] Open
Abstract
SUMMARY The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug-target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug-target affinity. We show that graph neural networks not only predict drug-target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug-target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. AVAILABILITY OF IMPLEMENTATION The proposed models are implemented in Python. Related data, pre-trained models and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thin Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia
| | - Hang Le
- Faculty of Information Technology, Nha Trang University, Nha Trang, Khanh Hoa, Viet Nam
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia
| | - Tri Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia
| | - Thuc Duy Le
- School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, 5095, Australia
| | - Svetha Venkatesh
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia
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22
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Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11072914] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was constructed for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease.
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23
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Bekono BD, Sona AN, Eni DB, Owono LCO, Megnassan E, Ntie-Kang F. Molecular mechanics approaches for rational drug design: forcefields and solvation models. PHYSICAL SCIENCES REVIEWS 2021. [DOI: 10.1515/psr-2019-0128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
The use of molecular mechanics (MM) in understanding the energy and target of a drug, its structures, and properties has increased recently. This is achieved by the formulation of a simple MM energy equation, which represents the sum of the different energy interactions, often referred to as “forcefields” (FFs). The concept of FFs is now widely used as one of the fundamental tools for the in silico prediction of drug-target interactions. To generate more accurate predictions in the in silico drug discovery projects, the solvent effects are often taken into account. This review seeks to present an introductory guide for the reader on the fundamentals of MM with special emphasis on the role of FFs and the solvation models.
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Affiliation(s)
- Boris D. Bekono
- Department of Physics, Ecole Normale Supérieure , University of Yaoundé I , P.O. Box 47 , Yaoundé , Cameroon
| | - Alfred N. Sona
- Department of Chemistry , University of Buea , P.O. Box 63 , Buea , Cameroon
| | - Donatus B. Eni
- Department of Chemistry , University of Buea , P.O. Box 63 , Buea , Cameroon
- Department of Inorganic Chemistry, Faculty of Science , University of Yaoundé I , BP 812 , Yaoundé , Cameroon
| | - Luc C. O. Owono
- Department of Physics, Ecole Normale Supérieure , University of Yaoundé I , P.O. Box 47 , Yaoundé , Cameroon
- CEPAMOQ, Faculty of Science , University of Douala , P.O. Box 8580 , Douala , Cameroon
| | - Eugène Megnassan
- Laboratoire de Physique Fondamentale et Appliquée (LPFA) , University of Abobo-Adjamé (now Nangui Abrogoua) , Abidjan 02 , Côte d’Ivoire
| | - Fidele Ntie-Kang
- Department of Chemistry , University of Buea , P.O. Box 63 , Buea , Cameroon
- Department of Pharmaceutical Chemistry , Martin-Luther University Halle-Wittenberg , Kurt-Mothes Str. 4, 06120 Halle (Saale) , Germany
- Institut für Botanik , Technische Universität Dresden , Zellescher Weg 20b, 01062 Dresden , Germany
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Hassan R, Allali I, Agamah FE, Elsheikh SSM, Thomford NE, Dandara C, Chimusa ER. Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine. Brief Bioinform 2020; 22:6012864. [PMID: 33253350 DOI: 10.1093/bib/bbaa292] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/19/2020] [Accepted: 10/03/2020] [Indexed: 12/15/2022] Open
Abstract
Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.
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Affiliation(s)
- Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town
| | - Imane Allali
- Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Morocco
| | - Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town
| | | | - Nicholas E Thomford
- Lecturers at the Department of Medical Biochemistry School of Medical Sciences, University of Cape Coast, Ghana
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, University of Cape Town
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town
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25
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Squillario M, Abate G, Tomasi F, Tozzo V, Barla A, Uberti D. A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease. Sci Rep 2020; 10:12063. [PMID: 32694537 PMCID: PMC7374579 DOI: 10.1038/s41598-020-67699-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 06/10/2020] [Indexed: 12/18/2022] Open
Abstract
Genome–wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer’s disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.
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Affiliation(s)
| | - Giulia Abate
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | | | | | | | - Daniela Uberti
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
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26
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Wang X, Liu Y, Lu F, Li H, Gao P, Wei D. Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction. Front Bioeng Biotechnol 2020; 8:267. [PMID: 32318557 PMCID: PMC7147459 DOI: 10.3389/fbioe.2020.00267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 03/13/2020] [Indexed: 11/13/2022] Open
Abstract
Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.
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Affiliation(s)
- Xianfang Wang
- School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, China.,School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Yifeng Liu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Fan Lu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Hongfei Li
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Dongqing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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27
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Chen J, Zhang L. A survey and systematic assessment of computational methods for drug response prediction. Brief Bioinform 2020; 22:232-246. [PMID: 31927568 DOI: 10.1093/bib/bbz164] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Drug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancers. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to drug response prediction. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assess 17 representative methods for drug response prediction, which have been developed in the past 5 years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.
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28
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Park D, Lee J, Chung JJ, Jung Y, Kim SH. Integrating Organs-on-Chips: Multiplexing, Scaling, Vascularization, and Innervation. Trends Biotechnol 2019; 38:99-112. [PMID: 31345572 DOI: 10.1016/j.tibtech.2019.06.006] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 06/18/2019] [Accepted: 06/21/2019] [Indexed: 12/29/2022]
Abstract
Organs-on-chips (OoCs) have attracted significant attention because they can be designed to mimic in vivo environments. Beyond constructing a single OoC, recent efforts have tried to integrate multiple OoCs to broaden potential applications such as disease modeling and drug discoveries. However, various challenges remain for integrating OoCs towards in vivo-like operation, such as incorporating various connections for integrating multiple OoCs. We review multiplexed OoCs and challenges they face: scaling, vascularization, and innervation. In our opinion, future OoCs will be constructed to have increased predictive power for in vivo phenomena and will ultimately become a mainstream tool for high quality biomedical and pharmaceutical research.
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Affiliation(s)
- DoYeun Park
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jaeseo Lee
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Justin J Chung
- Biomaterials Research Center, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Youngmee Jung
- Biomaterials Research Center, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Soo Hyun Kim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea; Biomaterials Research Center, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea.
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29
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Yang JH, Wright SN, Hamblin M, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. Cell 2019; 177:1649-1661.e9. [PMID: 31080069 PMCID: PMC6545570 DOI: 10.1016/j.cell.2019.04.016] [Citation(s) in RCA: 188] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 03/19/2019] [Accepted: 04/08/2019] [Indexed: 12/13/2022]
Abstract
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.
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Affiliation(s)
- Jason H Yang
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah N Wright
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Meagan Hamblin
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Miguel A Alcantar
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Allison J Lopatkin
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Sangeeta Satish
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Amir Nili
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Graham C Walker
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James J Collins
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
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30
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Cacabelos R, Carril JC, Cacabelos N, Kazantsev AG, Vostrov AV, Corzo L, Cacabelos P, Goldgaber D. Sirtuins in Alzheimer's Disease: SIRT2-Related GenoPhenotypes and Implications for PharmacoEpiGenetics. Int J Mol Sci 2019; 20:ijms20051249. [PMID: 30871086 PMCID: PMC6429449 DOI: 10.3390/ijms20051249] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 03/07/2019] [Indexed: 12/11/2022] Open
Abstract
Sirtuins (SIRT1-7) are NAD+-dependent protein deacetylases/ADP ribosyltransferases with important roles in chromatin silencing, cell cycle regulation, cellular differentiation, cellular stress response, metabolism and aging. Sirtuins are components of the epigenetic machinery, which is disturbed in Alzheimer’s disease (AD), contributing to AD pathogenesis. There is an association between the SIRT2-C/T genotype (rs10410544) (50.92%) and AD susceptibility in the APOEε4-negative population (SIRT2-C/C, 34.72%; SIRT2-T/T 14.36%). The integration of SIRT2 and APOE variants in bigenic clusters yields 18 haplotypes. The 5 most frequent bigenic genotypes in AD are 33CT (27.81%), 33CC (21.36%), 34CT (15.29%), 34CC (9.76%) and 33TT (7.18%). There is an accumulation of APOE-3/4 and APOE-4/4 carriers in SIRT2-T/T > SIRT2-C/T > SIRT2-C/C carriers, and also of SIRT2-T/T and SIRT2-C/T carriers in patients who harbor the APOE-4/4 genotype. SIRT2 variants influence biochemical, hematological, metabolic and cardiovascular phenotypes, and modestly affect the pharmacoepigenetic outcome in AD. SIRT2-C/T carriers are the best responders, SIRT2-T/T carriers show an intermediate pattern, and SIRT2-C/C carriers are the worst responders to a multifactorial treatment. In APOE-SIRT2 bigenic clusters, 33CC carriers respond better than 33TT and 34CT carriers, whereas 24CC and 44CC carriers behave as the worst responders. CYP2D6 extensive metabolizers (EM) are the best responders, poor metabolizers (PM) are the worst responders, and ultra-rapid metabolizers (UM) tend to be better responders that intermediate metabolizers (IM). In association with CYP2D6 genophenotypes, SIRT2-C/T-EMs are the best responders. Some Sirtuin modulators might be potential candidates for AD treatment.
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Affiliation(s)
- Ramón Cacabelos
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165 Bergondo, Corunna, Spain.
| | - Juan C Carril
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165 Bergondo, Corunna, Spain.
| | - Natalia Cacabelos
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165 Bergondo, Corunna, Spain.
| | - Aleksey G Kazantsev
- Department of Psychiatry and Behavioral Science, Stony Brook University, Stony Brook, NY 11794, USA.
| | - Alex V Vostrov
- Department of Psychiatry and Behavioral Science, Stony Brook University, Stony Brook, NY 11794, USA.
| | - Lola Corzo
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165 Bergondo, Corunna, Spain.
| | - Pablo Cacabelos
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165 Bergondo, Corunna, Spain.
| | - Dmitry Goldgaber
- Department of Psychiatry and Behavioral Science, Stony Brook University, Stony Brook, NY 11794, USA.
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31
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De Lucia S, Tsamesidis I, Pau MC, Kesely KR, Pantaleo A, Turrini F. Induction of high tolerance to artemisinin by sub-lethal administration: A new in vitro model of P. falciparum. PLoS One 2018; 13:e0191084. [PMID: 29342187 PMCID: PMC5771598 DOI: 10.1371/journal.pone.0191084] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 12/18/2017] [Indexed: 12/13/2022] Open
Abstract
Artemisinin resistance is a major threat to malaria control efforts. Resistance is characterized by an increase in the Plasmodium falciparum parasite clearance half-life following treatment with artemisinin-based combination therapies (ACTs) and an increase in the percentage of surviving parasites. The remarkably short blood half-life of artemisinin derivatives may contribute to drug-resistance, possibly through factors including sub-lethal plasma concentrations and inadequate exposure. Here we selected for a new strain of artemisinin resistant parasites, termed the artemisinin resistant strain 1 (ARS1), by treating P. falciparum Palo Alto (PA) cultures with sub-lethal concentrations of dihydroartemisinin (DHA). The resistance phenotype was maintained for over 1 year through monthly maintenance treatments with low doses of 2.5 nM DHA. There was a moderate increase in the DHA IC50 in ARS1 when compared with parental strain PA after 72 h of drug exposure (from 0.68 nM to 2 nM DHA). In addition, ARS1 survived treatment physiologically relevant DHA concentrations (700 nM) observed in patients. Furthermore, we confirmed a lack of cross-resistance against a panel of antimalarials commonly used as partner drugs in ACTs. Finally, ARS1 did not contain Pfk13 propeller domain mutations associated with ART resistance in the Greater Mekong Region. With a stable growth rate, ARS1 represents a valuable tool for the development of new antimalarial compounds and studies to further elucidate the mechanisms of ART resistance.
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Affiliation(s)
- Serena De Lucia
- Department of Oncology, University of Turin, Turin, Italy
- * E-mail:
| | - Ioannis Tsamesidis
- Department of Medicine, Section of Internal Medicine, University of Verona, Verona, Italy
| | - Maria Carmina Pau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristina R. Kesely
- Department of Biochemistry, Purdue University, West Lafayette, United States of America
| | - Antonella Pantaleo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
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32
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Philip AM, Wang Y, Mauro A, El-Rass S, Marshall JC, Lee WL, Slutsky AS, dos Santos CC, Wen XY. Development of a zebrafish sepsis model for high-throughput drug discovery. Mol Med 2017; 23:134-148. [PMID: 28598490 PMCID: PMC5522968 DOI: 10.2119/molmed.2016.00188] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 05/23/2017] [Indexed: 12/22/2022] Open
Abstract
Sepsis is a leading cause of death worldwide. Current treatment modalities remain largely supportive. Intervention strategies focused on inhibiting specific mediators of the inflammatory host response have been largely unsuccessful, a consequence of an inadequate understanding of the complexity and heterogeneity of the innate immune response. Moreover, the conventional drug development pipeline is time consuming and expensive and the low success rates associated with cell-based screens underline the need for whole organism screening strategies, especially for complex pathological processes. Here, we established an LPS-induced zebrafish endotoxemia model, which exhibits the major hallmarks of human sepsis including, edema and tissue/organ damage, increased vascular permeability and vascular leakage accompanied by an altered expression of cellular junction proteins, increased cytokine expression, immune cell activation and ROS production, reduced circulation and increased platelet aggregation. We tested the suitability of the model for phenotype-based drug screening using three primary readouts: mortality, vascular leakage, and ROS production. Preliminary screening identified fasudil, a drug known to protect against vascular leakage in murine models, as a lead hit thereby validating the utility of our model for sepsis drug screens. This zebrafish sepsis model has the potential to rapidly analyze sepsis associated pathologies and cellular processes in the whole organism, as well as to screen and validate large numbers of compounds that can modify sepsis pathology in vivo.
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Affiliation(s)
- Anju M Philip
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Physiology, Toronto, Ontario, Canada
| | - Youdong Wang
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
| | - Antonio Mauro
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Medicine and Institute of Medical Science, Toronto, Ontario, Canada
- Collaborative Program in Cardiovascular Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Suzan El-Rass
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Medicine and Institute of Medical Science, Toronto, Ontario, Canada
- Collaborative Program in Cardiovascular Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John C Marshall
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Interdepartmental Division of Critical Care, Toronto, Ontario, Canada
| | - Warren L Lee
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Medicine and Institute of Medical Science, Toronto, Ontario, Canada
| | - Arthur S Slutsky
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Medicine and Institute of Medical Science, Toronto, Ontario, Canada
- Interdepartmental Division of Critical Care, Toronto, Ontario, Canada
| | - Claudia C dos Santos
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Medicine and Institute of Medical Science, Toronto, Ontario, Canada
- Interdepartmental Division of Critical Care, Toronto, Ontario, Canada
| | - Xiao-Yan Wen
- Zebrafish Centre for Advanced Drug Discovery, St. Michael’s Hospital, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Physiology, Toronto, Ontario, Canada
- Department of Medicine and Institute of Medical Science, Toronto, Ontario, Canada
- Collaborative Program in Cardiovascular Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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33
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Cacabelos R, Torrellas C, Teijido O, Carril JC. Pharmacogenetic considerations in the treatment of Alzheimer's disease. Pharmacogenomics 2016; 17:1041-74. [PMID: 27291247 DOI: 10.2217/pgs-2016-0031] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The practical pharmacogenetics of Alzheimer's disease (AD) is circumscribed to acetylcholinesterase inhibitors (AChEIs) and memantine. However, pharmacogenetic procedures should be applied to novel strategies in AD therapeutics including: novel AChEIs and neurotransmitter regulators, anti-Aβ treatments, anti-tau treatments, pleiotropic products, epigenetic drugs and combination therapies. Genes involved in the pharmacogenetic network are under the influence of the epigenetic machinery which regulates gene expression transcriptionally and post-transcriptionally, configuring the fundamentals of pharmacoepigenomics. Over 60% of AD patients present concomitant pathologies demanding additional treatments which increase the likelihood of drug-drug interactions. Lipid metabolism dysfunction is a pathogenic mechanism inherent to AD neurodegeneration. The therapeutic response to hypolipidemic compounds is influenced by the APOE and CYP genotypes. The development of novel compounds and the use of combination/multifactorial treatments require the implantation of pharmacogenomic procedures for the avoidance of ADRs and the optimization of therapeutics.
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Affiliation(s)
- Ramón Cacabelos
- Department of Genomic Medicine, Camilo José Cela University, Madrid, Spain.,EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
| | - Clara Torrellas
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
| | - Oscar Teijido
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
| | - Juan Carlos Carril
- EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, Corunna, Spain
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34
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Kalman LV, Agúndez JA, Appell ML, Black JL, Bell GC, Boukouvala S, Bruckner C, Bruford E, Bruckner C, Caudle K, Coulthard S, Daly AK, Del Tredici AL, den Dunnen JT, Drozda K, Everts R, Flockhart D, Freimuth R, Gaedigk A, Hachad H, Hartshorne T, Ingelman-Sundberg M, Klein TE, Lauschke VM, Maglott DR, McLeod HL, McMillin GA, Meyer UA, Müller DJ, Nickerson DA, Oetting WS, Pacanowski M, Pratt VM, Relling MV, Roberts A, Rubinstein WS, Sangkuhl K, Schwab M, Scott SA, Sim SC, Thirumaran RK, Toji LH, Tyndale R, van Schaik RHN, Whirl-Carrillo M, Yeo KTJ, Zanger UM. Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting. Clin Pharmacol Ther 2016; 99:172-85. [PMID: 26479518 PMCID: PMC4724253 DOI: 10.1002/cpt.280] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Revised: 10/13/2015] [Accepted: 10/14/2015] [Indexed: 12/21/2022]
Abstract
This article provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.
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Affiliation(s)
- Lisa V. Kalman
- Centers for Disease, Control and Prevention, 1600 Clifton Rd, MSG23, Atlanta GA 30333, 404 498-2707, 404 498-2231
| | - José A.G. Agúndez
- Dept. Pharmacology, University of Extremadura, Avda de la, Universidad s/n., 10071 Cáceres, SPAIN, +34924289458, +34927257000
| | - Malin Lindqvist Appell
- Department of Medical and Health sciences, Faculty of Medicine and Health Sciences, Linköping University, Division of Drug Research, Linköping University, SE-581 83, LINKÖPING, +4613286880
| | | | - Gillian C. Bell
- Moffitt Cancer Center, 12902 Magnolia Dr Tampa, FL 33612, 813-745-6525, 813-745-3882
| | - Sotiria Boukouvala
- Democritus University of Thrace, Department of Molecular Biology and Genetics, Building 10, University Campus, Alexandroupolis 68100, Greece, +30-25510-30613, +30-25510-30632
| | - Carsten Bruckner
- Affymetrix, 3420 Central Expy, Santa Clara, CA 95051, USA, 1-408-731-5879
| | - Elspeth Bruford
- HUGO Gene, Nomenclature, Committee (HGNC), EMBL-EBI, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK, +44-1223-494468, +44-1223-492624
| | - Carsten Bruckner
- Affymetrix, 3420 Central Expy, Santa Clara, CA 95051, USA, 1-408-731-5879
| | - Kelly Caudle
- St. Jude Children’s Research Hospital, 262 Danny Thomas Place, MS 313 Memphis, TN 38105, 901-595-3125, 901-595-3994
| | - Sally Coulthard
- Newcastle University, Institute for Cellular Medicine, William Leech Building, Newcastle Medical School, Framlington Place, Newcastle University NE2 4HH UK, +44 1912080723, +44 1912085232
| | - Ann K. Daly
- Newcastle University, Institute of Cellular Medicine, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK, None, 44-191-208-7031
| | - Andria L. Del Tredici
- Millennium Health, LLC, 16981 Via Tazon, San Diego, CA 92127, none, (858) 451-3535 x1682
| | - Johan T den Dunnen
- Leiden University Medical Center, Human Genetics and Clinical Genetics, PO Box 9600, 2300RC Leiden, Nederland, none, +31-71-5269501
| | - Katarzyna Drozda
- Food and Drug Administration, 10903 New Hampshire Ave. Silver Spring, MD 20993, 240 402-0422
| | - Robin Everts
- Agena Bioscience, 3565 General Atomics Court, San Diego, CA 92121, None, +1 858-882-2655
| | - David Flockhart
- Indiana University, 950 W. Walnut St., room 402, Indianaplis, IN 46202, 317-274-2810
| | - Robert Freimuth
- Mayo Clinic, 200 First Street SW Rochester, MN 55905, 507-284-0753
| | - Andrea Gaedigk
- Division of Clinical Pharmacology & Therapeutic Innovation, Children’s Mercy Kansas City and School of Medicine, University of Missouri-Kansas City, 2401 Gillham Road, Kansas City, MO 64108, 816-234-1958, 816-234-3941
| | - Houda Hachad
- Translational Software, 12410 SE 32 Street Suite 150, Bellevue, WA 98005, 206-777-4132
| | - Toinette Hartshorne
- Genetic Analysis, Thermo Fisher Scientific, 180 Oyster Point Blvd. South San Francisco, CA 94080, 650-244-1669, 650-246-4080
| | - Magnus Ingelman-Sundberg
- Karolinska Institutet, Department of Physiology and Pharmacology, Nanna Svartz väg 2, 17177 Stockholm, SwedenSE, +468337327, +46852487735+
| | - Teri E. Klein
- Department of Genetics, Stanford University, 443 Via Ortega Avenue, Stanford, CA 94305, 650-725-3863, 650-736-0156
| | - Volker M. Lauschke
- Karolinska Institutet, Department of Physiology and Pharmacology, Nanna Svartz väg 2, 17177 Stockholm, Sweden, +46 8-337327, +46 8-5248-7711
| | - Donna R. Maglott
- National Institutes of Health / National Library of Medicine / National Center for Biotechnology Information, 45 Center Drive, Bethesda, MD 20894, 301 435-4895
| | - Howard L. McLeod
- Moffitt Cancer Center, 12902 Magnolia Drive, Tampa FL 33612, 813-745-3347
| | - Gwendolyn A. McMillin
- University of Utah and ARUP Laboratories, 500 Chipeta Way, Salt Lake City UT 84108, 801-584-5207, 801-583-2787
| | - Urs A. Meyer
- University of Basel, Biozentrum, Klingelbergstrasse 50/70, CH 4056, Basel, Switzerland, +41612672208, +41 61 267 2220
| | - Daniel J. Müller
- Dept. of Psychiatry, University of Toronto, CAMH, 250 College ST., R132, 416 979 4666, 416 535 8501 (x. 36851)
| | - Deborah A. Nickerson
- University of Washington, Department of Genome Sciences, Box 355065, Seattle, WA, 98195-5065, 206-221-6498, 206-685-7387
| | - William S. Oetting
- Experimental and Clinical Pharmacology, University of Minnesota, 7-115 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN 55455, 612-624-6645, 612-624-1139
| | - Michael Pacanowski
- U.S. Food and Drug Administration, 10903 New Hampshire Ave., WO Building 51, Rm 2132, HFD870, Silver Spring, MD 20993, 301-847-8720, 301-796-3919
| | - Victoria M. Pratt
- Indiana University School of Medicine, 975 W. Walnut St., IB-130, Indianapolis IN 46202, 317-274-2293, 317-274-8322
| | - Mary V. Relling
- Chair, Pharmaceutical Dept., St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Room I-5112 Memphis, TN 38105, ph 901 595 2348, fax 901 595 8869
| | - Ali Roberts
- Aegis Science Corporation, 515 Great Circle Road, Nashville, TN 37228, 615-255-3030, 615-477-9429
| | - Wendy S. Rubinstein
- National Institutes of Health / National Library of Medicine / National Center for Biotechnology Information, 45 Center Drive, Bethesda, MD 20894, 301.480.4023, 301.435.5991
| | - Katrin Sangkuhl
- Stanford University, 443 Via Ortega, Room 213, MC4245, Stanford CA 94305, 650-725-3863, 650-725-0659
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch- Institute of Clinical Pharmacology, Stuttgart and Department of Clinical Pharmacology, University Hospital, Tuebingen, Germany, Auerbachstrasse 112, 70378 Stuttgart, +49 711 859295, +49 711 8101 3700
| | - Stuart A. Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1487, 212-241-0139, 212-241-3780
| | - Sarah C Sim
- Karolinska Institutet, Department of Physiology and Pharmacology, Nanna Svartz Väg 2, 171 77 Stockholm, Sweden, +468337327, +46852487735
| | - Ranjit K Thirumaran
- Genelex Corporation, 3101 Western Ave., Suite 100, Seattle, WA 98121., 206 219-4000, 206 826-1926
| | - Lorraine H. Toji
- Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, 856 757-9719
| | - Rachel Tyndale
- CAMH and Departments of Psychiatry, Pharmacology and Toxicology, University of Toronto, Rm 4326, Department of Pharmacology, 1 King’s College Circle, Toronto, Canada, M5S 1A8., 416 978-6395, 416 978-6374
| | - Ron HN van Schaik
- 1Dept Clinical Chemistry, Erasmus MC Rotterdam; 2IFCC Task Force Pharmacogenetics, Room Na-415; Wytemaweg 80, 3015CN Rotterdam, The Netherlands, +31-10-7033119
| | - Michelle Whirl-Carrillo
- Department of Genetics, Stanford University, 443 Via Ortega, Rm 213 Stanford, CA 94305, 650-725-3863, 650-725-0659
| | - Kiang-Teck J Yeo
- Department of Pathology, The University of Chicago, 5841 S Maryland Ave, MC 0004, TW010, Chicago, IL 60637, 773-702-6268, 773-702-1318
| | - Ulrich M. Zanger
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Auerbachstrasse 112, Stuttgart, 70376, Germany, +49-711-859295, +49-711-81013704
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Vinca alkaloids in the therapeutic management of malignant pleural mesothelioma. Cancer Treat Rev 2015; 41:853-8. [DOI: 10.1016/j.ctrv.2015.10.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2015] [Revised: 10/17/2015] [Accepted: 10/22/2015] [Indexed: 01/06/2023]
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Cacabelos R, Torrellas C, Carrera I. Opportunities in pharmacogenomics for the treatment of Alzheimer's disease. FUTURE NEUROLOGY 2015. [DOI: 10.2217/fnl.15.12] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
ABSTRACT In Alzheimer's disease (AD), approximately 10–20% of direct costs are associated with pharmacological treatment. Pharmacogenomics account for 30–90% variability in pharmacokinetics and pharmacodynamics. Genes potentially involved in the pharmacogenomics outcome include pathogenic, mechanistic, metabolic, transporter and pleiotropic genes. Over 75% of the Caucasian population is defective for the CYP2D6+2C9+2C19 cluster. Polymorphic variants in the APOE-TOMM40 region influence AD pharmacogenomics. APOE-4 carriers are the worst responders and APOE-3 carriers are the best responders to conventional treatments. TOMM40 poly T-S/S carriers are the best responders, VL/VL and S/VL carriers are intermediate responders and L/L carriers are the worst responders. The haplotype 4/4-L/L is probably responsible for early onset of the disease, a faster cognitive decline and a poor response to different treatments.
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Affiliation(s)
- Ramón Cacabelos
- Camilo José Cela University, Villanueva de la Cañada, 28692-Madrid, Spain
- EuroEspes Biomedical Research Center, Institute of Medical Science & Genomic Medicine, Corunna, Spain
| | - Clara Torrellas
- Camilo José Cela University, Villanueva de la Cañada, 28692-Madrid, Spain
- EuroEspes Biomedical Research Center, Institute of Medical Science & Genomic Medicine, Corunna, Spain
| | - Iván Carrera
- Camilo José Cela University, Villanueva de la Cañada, 28692-Madrid, Spain
- EuroEspes Biomedical Research Center, Institute of Medical Science & Genomic Medicine, Corunna, Spain
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Ostergren JE, Dingel MJ, McCormick JB, Koenig BA. Unwarranted optimism in media portrayals of genetic research on addiction overshadows critical ethical and social concerns. JOURNAL OF HEALTH COMMUNICATION 2015; 20:555-65. [PMID: 25806781 PMCID: PMC4451206 DOI: 10.1080/10810730.2014.999895] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The cost of addiction in the United States, in combination with a host of new tools and techniques, has fueled an explosion of genetic research on addiction. Because the media has the capacity to reflect and influence public perception, there is a need to examine how treatments and preventive approaches projected to emerge from addiction genetic research are presented to the public. The authors conducted a textual analysis of 145 news articles reporting on genetic research on addiction from popular print media in the United States and from popular news and medical internet sites. In articles that report on prevention, the media emphasize vaccine development and identifying individuals at genetic risk through population screening. Articles that emphasize treatment often promote current pharmaceutical solutions and highlight the possibility of tailoring treatments to specific genetic variants. The authors raise concerns about the tendency of this coverage to focus on the benefits of pharmaceutical treatments and genetic-based approaches to prevention while neglecting or downplaying potential risks and ethical issues. This analysis suggests a need for more balanced, evidence-based media reporting on the potential outcomes of genetic research.
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Affiliation(s)
- Jenny E. Ostergren
- Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Molly J. Dingel
- Center for Learning Innovation, University of Minnesota Rochester
| | | | - Barbara A. Koenig
- Institute for Health & Aging, University of California, San Francisco
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Maitland ML, Xu CF, Cheng YC, Kistner-Griffin E, Ryan KA, Karrison TG, Das S, Torgerson D, Gamazon ER, Thomeas V, Levine MR, Wilson PA, Bing N, Liu Y, Cardon LR, Pandite LN, O'Connell JR, Cox NJ, Mitchell BD, Ratain MJ, Shuldiner AR. Identification of a variant in KDR associated with serum VEGFR2 and pharmacodynamics of Pazopanib. Clin Cancer Res 2014; 21:365-72. [PMID: 25411163 DOI: 10.1158/1078-0432.ccr-14-1683] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE VEGF receptor (VEGFR) kinases are important drug targets in oncology that affect function of systemic endothelial cells. To discover genetic markers that affect VEGFR inhibitor pharmacodynamics, we performed a genome-wide association study of serum soluble vascular VEGFR2 concentrations [sVEGFR2], a pharmacodynamic biomarker for VEGFR2 inhibitors. EXPERIMENTAL DESIGN We conducted a genome-wide association study (GWAS) of [sVEGFR2] in 736 healthy Old Order Amish volunteers. Gene variants identified from the GWAS were genotyped serially in a cohort of 128 patients with advanced solid tumor with baseline [sVEGFR2] measurements, and in 121 patients with renal carcinoma with [sVEGFR2] measured before and during pazopanib therapy. RESULTS rs34231037 (C482R) in KDR, the gene encoding sVEGFR2 was found to be highly associated with [sVEGFR2], explaining 23% of the variance (P = 2.7 × 10(-37)). Association of rs34231037 with [sVEGFR2] was replicated in 128 patients with cancer with comparable effect size (P = 0.025). Furthermore, rs34231037 was a significant predictor of changes in [sVEGFR2] in response to pazopanib (P = 0.01). CONCLUSION Our findings suggest that genome-wide analysis of phenotypes in healthy populations can expedite identification of candidate pharmacogenetic markers. Genotyping for germline variants in KDR may have clinical utility in identifying patients with cancer with unusual sensitivity to effects of VEGFR2 kinase inhibitors.
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Affiliation(s)
- Michael L Maitland
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois. Committee on Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, Illinois. Comprehensive Cancer Center, University of Chicago, Chicago, Illinois.
| | - Chun-Fang Xu
- Glaxo SmithKline Genetics, Stevenage, United Kingdom
| | - Yu-Ching Cheng
- Program in Personalized and Genomic Medicine, and Division of Endocrinology, Diabetes and Nutrition, School of Medicine, University of Maryland, Baltimore, Maryland
| | | | - Kathleen A Ryan
- Program in Personalized and Genomic Medicine, and Division of Endocrinology, Diabetes and Nutrition, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Theodore G Karrison
- Comprehensive Cancer Center, University of Chicago, Chicago, Illinois. Department of Health Studies, University of Chicago, Chicago, Illinois
| | - Soma Das
- Comprehensive Cancer Center, University of Chicago, Chicago, Illinois. Department of Human Genetics, University of Chicago, Chicago, Illinois
| | - Dara Torgerson
- Department of Human Genetics, University of Chicago, Chicago, Illinois
| | - Eric R Gamazon
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Vasiliki Thomeas
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Matthew R Levine
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Paul A Wilson
- Glaxo SmithKline Computation Biology, Stevenage, United Kingdom
| | - Nan Bing
- Glaxo SmithKline Genetics, Research Triangle Park, North Carolina
| | - Yuan Liu
- Glaxo SmithKline Oncology, Philadelphia, Pennsylvania
| | - Lon R Cardon
- Glaxo SmithKline Genetics, Philadelphia, Pennsylvania
| | - Lini N Pandite
- Glaxo SmithKline Oncology, Research Triangle Park, North Carolina
| | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, and Division of Endocrinology, Diabetes and Nutrition, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Nancy J Cox
- Committee on Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, Illinois. Comprehensive Cancer Center, University of Chicago, Chicago, Illinois. Department of Human Genetics, University of Chicago, Chicago, Illinois. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, and Division of Endocrinology, Diabetes and Nutrition, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Mark J Ratain
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois. Committee on Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, Illinois. Comprehensive Cancer Center, University of Chicago, Chicago, Illinois
| | - Alan R Shuldiner
- Program in Personalized and Genomic Medicine, and Division of Endocrinology, Diabetes and Nutrition, School of Medicine, University of Maryland, Baltimore, Maryland. Geriatric Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland
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Ostrovidov S, Hosseini V, Ahadian S, Fujie T, Parthiban SP, Ramalingam M, Bae H, Kaji H, Khademhosseini A. Skeletal muscle tissue engineering: methods to form skeletal myotubes and their applications. TISSUE ENGINEERING. PART B, REVIEWS 2014; 20:403-36. [PMID: 24320971 PMCID: PMC4193686 DOI: 10.1089/ten.teb.2013.0534] [Citation(s) in RCA: 163] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Accepted: 12/05/2013] [Indexed: 12/25/2022]
Abstract
Skeletal muscle tissue engineering (SMTE) aims to repair or regenerate defective skeletal muscle tissue lost by traumatic injury, tumor ablation, or muscular disease. However, two decades after the introduction of SMTE, the engineering of functional skeletal muscle in the laboratory still remains a great challenge, and numerous techniques for growing functional muscle tissues are constantly being developed. This article reviews the recent findings regarding the methodology and various technical aspects of SMTE, including cell alignment and differentiation. We describe the structure and organization of muscle and discuss the methods for myoblast alignment cultured in vitro. To better understand muscle formation and to enhance the engineering of skeletal muscle, we also address the molecular basics of myogenesis and discuss different methods to induce myoblast differentiation into myotubes. We then provide an overview of different coculture systems involving skeletal muscle cells, and highlight major applications of engineered skeletal muscle tissues. Finally, potential challenges and future research directions for SMTE are outlined.
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Affiliation(s)
- Serge Ostrovidov
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai, Japan
| | - Vahid Hosseini
- Laboratory of Applied Mechanobiology, Department of Health Sciences and Technology, ETH, Zurich, Switzerland
| | - Samad Ahadian
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai, Japan
| | - Toshinori Fujie
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai, Japan
- Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
| | | | - Murugan Ramalingam
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai, Japan
- Faculté de Chirurgie Dentaire, Université de Strasbourg, Strasbourg Cedex, France
- Centre for Stem Cell Research, Christian Medical College Campus, Vellore, India
| | - Hojae Bae
- College of Animal Bioscience and Technology, Department of Bioindustrial Technologies, Konkuk University, Hwayang-dong, Kwangjin-gu, Seoul, Republic of Korea
| | - Hirokazu Kaji
- Department of Bioengineering and Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Ali Khademhosseini
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai, Japan
- Department of Maxillofacial Biomedical Engineering, Institute of Oral Biology, School of Dentistry, Kyung Hee University, Seoul, Republic of Korea
- Center for Biomedical Engineering, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States
- Department of Physics, King Abdulaziz University, Jeddah, Saudi Arabia
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Pieramico V, Esposito R, Cesinaro S, Frazzini V, Sensi SL. Effects of non-pharmacological or pharmacological interventions on cognition and brain plasticity of aging individuals. Front Syst Neurosci 2014; 8:153. [PMID: 25228860 PMCID: PMC4151335 DOI: 10.3389/fnsys.2014.00153] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 08/05/2014] [Indexed: 12/21/2022] Open
Abstract
Brain aging and aging-related neurodegenerative disorders are major health challenges faced by modern societies. Brain aging is associated with cognitive and functional decline and represents the favourable background for the onset and development of dementia. Brain aging is associated with early and subtle anatomo-functional physiological changes that often precede the appearance of clinical signs of cognitive decline. Neuroimaging approaches unveiled the functional correlates of these alterations and helped in the identification of therapeutic targets that can be potentially useful in counteracting age-dependent cognitive decline. A growing body of evidence supports the notion that cognitive stimulation and aerobic training can preserve and enhance operational skills in elderly individuals as well as reduce the incidence of dementia. This review aims at providing an extensive and critical overview of the most recent data that support the efficacy of non-pharmacological and pharmacological interventions aimed at enhancing cognition and brain plasticity in healthy elderly individuals as well as delaying the cognitive decline associated with dementia.
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Affiliation(s)
- Valentina Pieramico
- Molecular Neurology Unit, Center of Excellence on Aging, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy
| | - Roberto Esposito
- Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy
| | - Stefano Cesinaro
- Molecular Neurology Unit, Center of Excellence on Aging, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy
| | - Valerio Frazzini
- Molecular Neurology Unit, Center of Excellence on Aging, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy
| | - Stefano L Sensi
- Molecular Neurology Unit, Center of Excellence on Aging, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy ; Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy ; Departments of Neurology and Pharmacology, Institute for Memory Impairments and Neurological Disorders, University of California-Irvine Irvine, CA, USA
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41
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Naci H, Ioannidis JPA. How good is "evidence" from clinical studies of drug effects and why might such evidence fail in the prediction of the clinical utility of drugs? Annu Rev Pharmacol Toxicol 2014; 55:169-89. [PMID: 25149917 DOI: 10.1146/annurev-pharmtox-010814-124614] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Promising evidence from clinical studies of drug effects does not always translate to improvements in patient outcomes. In this review, we discuss why early evidence is often ill suited to the task of predicting the clinical utility of drugs. The current gap between initially described drug effects and their subsequent clinical utility results from deficits in the design, conduct, analysis, reporting, and synthesis of clinical studies-often creating conditions that generate favorable, but ultimately incorrect, conclusions regarding drug effects. There are potential solutions that could improve the relevance of clinical evidence in predicting the real-world effectiveness of drugs. What is needed is a new emphasis on clinical utility, with nonconflicted entities playing a greater role in the generation, synthesis, and interpretation of clinical evidence. Clinical studies should adopt strong design features, reflect clinical practice, and evaluate outcomes and comparisons that are meaningful to patients. Transformative changes to the research agenda may generate more meaningful and accurate evidence on drug effects to guide clinical decision making.
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Affiliation(s)
- Huseyin Naci
- LSE Health, London School of Economics and Political Science, London WC2A 2AE, United Kingdom;
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42
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Brown AMK, Renaud Y, Ross C, Hansen M, Mongrain I, Valois D, Carleton BC, Hayden MR, Dubé MP, Tardif JC, Phillips MS. Development of a broad-based ADME panel for use in pharmacogenomic studies. Pharmacogenomics 2014; 15:1185-95. [DOI: 10.2217/pgs.14.81] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: To optimally address the interindividual variability observed in pharmacokinetic drug response, we have created a custom genotyping panel that interrogates most of the key genetic variations present in a set of 181 prioritized genes responsible for the absorption, distribution, metabolism and excretion (ADME) of many therapeutic agents. This consensus list of genes and variants was based on the ADME core and extended gene lists compiled by a group of pharmaceutical companies as having relevance. Although these pharmacokinetic genes and pathways are well known, tools that can interrogate a large number of these genes simultaneously within a single experiment are not currently available. Methods: Using novel design strategies, we have developed an optimized and validated ADME genotyping panel, encompassing approximately 3000 variants, that has broad applicability to any study or clinical trial that would benefit from the evaluation of an extensive list of ADME genes. Results & conclusion: Over the course of three design iterations, overall assay conversion rates were improved from 83 to 97% resulting in a panel that fills in many of the gaps in coverage present on currently available commercial genotyping assays. The utility of the assay has been demonstrated by the screening of more than 1000 samples resulting in the discovery of novel pharmacogenomic associations. The assay, and the underlying methods, will continue to be a valuable tool for use in future pharmacogenomic studies. Original submitted 28 November 2013; Revision submitted 13 May 2014
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Affiliation(s)
- Andrew MK Brown
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
- Current affiliation: Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Yannick Renaud
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
| | - Colin Ross
- Division of Translational Therapeutics, Department of Pediatrics & Child & Family Research Institute, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | - Ian Mongrain
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
| | - Diane Valois
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
| | - Bruce C Carleton
- Division of Translational Therapeutics, Department of Pediatrics & Child & Family Research Institute, University of British Columbia, Vancouver, BC, Canada
- Pharmaceutical Outcomes Programme, BC Children's Hospital, Vancouver, BC, Canada
| | - Michael R Hayden
- Centre for Molecular Medicine & Therapeutics, Child & Family Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Marie-Pierre Dubé
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Jean-Claude Tardif
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Michael S Phillips
- Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada
- Montreal Heart Institute Research Centre, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
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Abstract
OBJECTIVE As genotyping technology has progressed, genome-wide association studies (GWAS) have matured into efficient and effective tools for mapping genes underlying human phenotypes. METHODS Recent studies have shown the utility of the GWAS approach for examining pharmacogenomic traits, including drug metabolism, efficacy, and toxicity. RESULTS Application of GWAS to pharmacogenomic outcomes presents unique challenges and opportunities. CONCLUSION In the current review, we discuss the potential promises and potential caveats of this approach specifically as it relates to pharmacogenomic studies. Concerns with study design, power and sample size, and analysis are reviewed. We further examine the features of successful pharmacogenomic GWAS, and describe consortia efforts that are likely to expand the reach of pharmacogenomic GWAS in the future.
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44
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Roses AD, Saunders AM, Lutz MW, Zhang N, Hariri AR, Asin KE, Crenshaw DG, Budur K, Burns DK, Brannan SK. New applications of disease genetics and pharmacogenetics to drug development. Curr Opin Pharmacol 2014; 14:81-9. [PMID: 24565016 DOI: 10.1016/j.coph.2013.12.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 12/03/2013] [Accepted: 12/03/2013] [Indexed: 11/28/2022]
Abstract
TOMMORROW is a Phase III delay of onset clinical trial to determine whether low doses of pioglitazone, a molecule that induces mitochondrial doubling, delays the onset of MCI-AD in normal subjects treated with low dose compared to placebo. BOLD imaging studies in rodents and man were used to find the dose that increases oxygen consumption at central regions of the brain in higher proportion than activation of large corticol regions. The trial is made practical by the use of a pharmacogenetic algorithm based on TOMM40 and APOE genotypes and age to identify normal subjects at high risk of MCI-AD between the ages of 65-83 years within a five year follow-up period.
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Affiliation(s)
- Allen D Roses
- Zinfandel Pharmaceuticals, Inc., Durham, NC, United States; Duke University Medical Center, Department of Neurology, Durham, NC, United States.
| | - Ann M Saunders
- Duke University Medical Center, Department of Neurology, Durham, NC, United States
| | - Michael W Lutz
- Duke University Medical Center, Department of Neurology, Durham, NC, United States
| | - Nanyin Zhang
- Pennsylvania State University, Department of Biomedical Engineering, State College, PA, United States
| | - Ahmad R Hariri
- Duke University Medical Center, Department of Neurology, Durham, NC, United States
| | - Karen E Asin
- Takeda Pharmaceuticals, Deerfield, IL, United States
| | | | - Kumar Budur
- Takeda Pharmaceuticals, Deerfield, IL, United States
| | - Daniel K Burns
- Zinfandel Pharmaceuticals, Inc., Durham, NC, United States
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Gharani N, Keller MA, Stack CB, Hodges LM, Schmidlen TJ, Lynch DE, Gordon ES, Christman MF. The Coriell personalized medicine collaborative pharmacogenomics appraisal, evidence scoring and interpretation system. Genome Med 2013; 5:93. [PMID: 24134832 PMCID: PMC3978656 DOI: 10.1186/gm499] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 09/16/2013] [Indexed: 12/15/2022] Open
Abstract
Implementation of pharmacogenomics (PGx) in clinical care can lead to improved drug efficacy and reduced adverse drug reactions. However, there has been a lag in adoption of PGx tests in clinical practice. This is due in part to a paucity of rigorous systems for translating published clinical and scientific data into standardized diagnostic tests with clear therapeutic recommendations. Here we describe the Pharmacogenomics Appraisal, Evidence Scoring and Interpretation System (PhAESIS), developed as part of the Coriell Personalized Medicine Collaborative research study, and its application to seven commonly prescribed drugs.
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Affiliation(s)
- Neda Gharani
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
| | - Margaret A Keller
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA ; Current Address: American Red Cross, 700 Spring Garden Street, Philadelphia, PA 19123, USA
| | - Catharine B Stack
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA ; Current Address: Annals of Internal Medicine, 190 N. Independence Mall West, Philadelphia, PA 19106, USA
| | - Laura M Hodges
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
| | - Tara J Schmidlen
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
| | - Daniel E Lynch
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
| | - Erynn S Gordon
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
| | - Michael F Christman
- The Coriell Institute for Medical Research, 403 Haddon Avenue, Camden, NJ 08103, USA
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State-of-the-Art Technologies to Interrogate Genetic/Genomic Components of Drug Response. CURRENT GENETIC MEDICINE REPORTS 2013. [DOI: 10.1007/s40142-013-0022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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47
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Godman B, Finlayson AE, Cheema PK, Zebedin-Brandl E, Gutiérrez-Ibarluzea I, Jones J, Malmström RE, Asola E, Baumgärtel C, Bennie M, Bishop I, Bucsics A, Campbell S, Diogene E, Ferrario A, Fürst J, Garuoliene K, Gomes M, Harris K, Haycox A, Herholz H, Hviding K, Jan S, Kalaba M, Kvalheim C, Laius O, Lööv SA, Malinowska K, Martin A, McCullagh L, Nilsson F, Paterson K, Schwabe U, Selke G, Sermet C, Simoens S, Tomek D, Vlahovic-Palcevski V, Voncina L, Wladysiuk M, van Woerkom M, Wong-Rieger D, Zara C, Ali R, Gustafsson LL. Personalizing health care: feasibility and future implications. BMC Med 2013; 11:179. [PMID: 23941275 PMCID: PMC3750765 DOI: 10.1186/1741-7015-11-179] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Accepted: 07/09/2013] [Indexed: 01/11/2023] Open
Abstract
Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.
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Affiliation(s)
- Brian Godman
- Department of Laboratory Medicine, Division of Clinical Pharmacology, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86, Stockholm, Sweden
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
- National Institute for Science and Technology on Innovation on Neglected Diseases, Centre for Technological Development in Health, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Alexander E Finlayson
- King’s Centre for Global Health, Global Health Offices, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK
| | - Parneet K Cheema
- Sunnybrook Odette Cancer Centre, 2075 Bayview Avenue, Toronto, ON, Canada
| | - Eva Zebedin-Brandl
- Hauptverband der Österreichischen Sozialversicherungsträger, 21 Kundmanngasse, AT-1031, Wien, Austria
- Institute of Pharmacology and Toxicology, Department for Biomedical Sciences, University of Vienna, Vienna, Austria
| | - Inaki Gutiérrez-Ibarluzea
- Osteba Basque Office for HTA, Ministry of Health of the Basque Country, Donostia-San Sebastian 1, 01010, Vitoria-Gasteiz, Basque Country, Spain
| | - Jan Jones
- NHS Tayside, Kings Cross, Dundee DD3 8EA, UK
| | - Rickard E Malmström
- Department of Medicine, Clinical Pharmacology Unit, Karolinska Institutet, Karolinska University Hospital Solna, SE-17176, Stockholm, Sweden
| | - Elina Asola
- Pharmaceutical Pricing Board, Ministry of Social Affairs and Health, PO Box 33, FI-00023 Government, Helsinki, Finland
| | | | - Marion Bennie
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
- Public Health & Intelligence Strategic Business Unit, NHS National Services Scotland, Edinburgh EH12 9EB, UK
| | - Iain Bishop
- Public Health & Intelligence Strategic Business Unit, NHS National Services Scotland, Edinburgh EH12 9EB, UK
| | - Anna Bucsics
- Hauptverband der Österreichischen Sozialversicherungsträger, 21 Kundmanngasse, AT-1031, Wien, Austria
| | - Stephen Campbell
- Centre for Primary Care, Institute of Population Health, University of Manchester, Manchester M13 9PL, UK
- NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Manchester M13 9PL, UK
| | - Eduardo Diogene
- Unitat de Coordinació i Estratègia del Medicament, Direcció Adjunta d'Afers Assistencials, Catalan Institute of Health, Barcelona, Spain
| | - Alessandra Ferrario
- London School of Economics and Political Science, LSE Health, Houghton Street, London WC2A 2AE, UK
| | - Jurij Fürst
- Health Insurance Institute, Miklosiceva 24, SI-1507, Ljubljana, Slovenia
| | - Kristina Garuoliene
- Medicines Reimbursement Department, National Health Insurance Fund, Europas a. 1, Vilnius, Lithuania
| | - Miguel Gomes
- INFARMED, Parque da Saúde de Lisboa, Avenida do Brasil 53, 1749-004, Lisbon, Portugal
| | - Katharine Harris
- King’s Centre for Global Health, Global Health Offices, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK
| | - Alan Haycox
- Liverpool Health Economics Centre, University of Liverpool, Chatham Street, Liverpool L69 7ZH, UK
| | - Harald Herholz
- Kassenärztliche Vereinigung Hessen, 15 Georg Voigt Strasse, DE-60325, Frankfurt am Main, Germany
| | - Krystyna Hviding
- Norwegian Medicines Agency, Sven Oftedals vei 8, 0950, Oslo, Norway
| | - Saira Jan
- Clinical Programs, Pharmacy Management, Horizon Blue Cross Blue Shield of New Jersey, Newark, USA
| | - Marija Kalaba
- Republic Institute for Health Insurance, Jovana Marinovica 2, 11000, Belgrade, Serbia
| | | | - Ott Laius
- State Agency of Medicines, Nooruse 1, 50411, Tartu, Estonia
| | - Sven-Ake Lööv
- Department of Healthcare Development, Stockholm County Council, Stockholm, Sweden
| | - Kamila Malinowska
- HTA Consulting, Starowiślna Street, 17/3, 31-038, Cracow, Poland
- Public Health School, The Medical Centre of Postgraduate Education, Kleczewska Street, 61/63, 01-813, Warsaw, Poland
| | - Andrew Martin
- NHS Greater Manchester Commissioning Support Unit, Salford, Manchester, UK
| | - Laura McCullagh
- National Centre for Pharmacoeconomics, St James's Hospital, Dublin 8, Ireland
| | - Fredrik Nilsson
- Dental and Pharmaceuticals Benefits Agency (TLV), PO Box 22520 Flemingatan 7, SE-104, Stockholm, Sweden
| | | | - Ulrich Schwabe
- University of Heidelberg, Institute of Pharmacology, D-69120, Heidelberg, Germany
| | - Gisbert Selke
- Wissenschaftliches Institut der AOK (WIDO), Rosenthaler Straße 31, 10178, Berlin, Germany
| | | | - Steven Simoens
- KU Leuven Department of Pharmaceutical and Pharmacological Sciences, 3000, Leuven, Belgium
| | - Dominik Tomek
- Faculty of Pharmacy, Comenius University and Faculty of Medicine, Slovak Medical University, Bratislava, Slovakia
| | - Vera Vlahovic-Palcevski
- Unit for Clinical Pharmacology, University Hospital Rijeka, Krešimirova 42, 51000, Rijeka, Croatia
| | - Luka Voncina
- Ministry of Health, Republic of Croatia, Ksaver 200a, Zagreb, Croatia
| | | | - Menno van Woerkom
- Dutch Institute for Rational Use of Medicines, 3527 GV, Utrecht, Netherlands
| | - Durhane Wong-Rieger
- Institute for Optimizing Health Outcomes, 151 Bloor Street West, Suite 600, Toronto, ON M5S 1S4, Canada
| | - Corrine Zara
- Barcelona Health Region, Catalan Health Service, Esteve Terrades 30, 08023, Barcelona, Spain
| | - Raghib Ali
- INDOX Cancer Research Network, Cancer Epidemiology Unit, University of Oxford, Oxford, UK
| | - Lars L Gustafsson
- Department of Laboratory Medicine, Division of Clinical Pharmacology, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86, Stockholm, Sweden
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Bochud M, Guessous I. Gene-environment interactions of selected pharmacogenes in arterial hypertension. Expert Rev Clin Pharmacol 2013; 5:677-86. [PMID: 23234325 DOI: 10.1586/ecp.12.58] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Hypertension affects approximately 1 billion people worldwide. Owing to population aging, hypertension-related cardiovascular burden is expected to rise in the near future. In addition to genetic variants influencing the blood pressure response to antihypertensive drugs, several genes encoding for drug-metabolizing or -transporting enzymes have been associated with blood pressure and/or hypertension in humans (e.g., ACE, CYP1A2, CYP3A5, ABCB1 and MTHFR) regardless of drug treatment. These genes are also involved in the metabolism and transport of endogenous substances and their effects may be modified by selected environmental factors, such as diet or lifestyle. However, little is currently known on the complex interplay between environmental factors, endogenous factors, genetic variants and drugs on blood pressure control. This review will discuss the respective role of population-based primary prevention and personalized medicine for arterial hypertension, taking a pharmacogenomics' perspective focusing on selected pharmacogenes.
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Affiliation(s)
- Murielle Bochud
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Switzerland.
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49
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Finlayson AE, Godman B, Paterson K, Aston E, Haycox A, Gustafsson LL, Ali R. Personalizing healthcare: from genetics through payment to improving care? J R Soc Med 2013; 106:41-4. [PMID: 23401634 DOI: 10.1258/jrsm.2012.120193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Affiliation(s)
- A E Finlayson
- King's Centre for Global Health, Global Health Offices, Weston Education Centre, London, UK.
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50
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Khabour OF, Alzoubi KH, Al-Azzam SI, Mhaidat NM. Frequency of MDR1 single nucleotide polymorphisms in a Jordanian population, including a novel variant. GENETICS AND MOLECULAR RESEARCH 2013; 12:801-8. [PMID: 23546964 DOI: 10.4238/2013.march.13.9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The multidrug resistance gene (MDR1 or ABCB1) codes for P-glycoprotein, which plays an important role in regulating absorption, distribution, and elimination of drugs. We examined MDR1 gene variants in 100 unrelated subjects from various regions of Jordan. The MDR1 gene was scanned using direct sequencing. Six rare variants in MDR1 were detected, including a new variant, T3075A. This variant did not affect the protein sequence (synonym for threonine). Among the common SNPs, the frequencies of rs1128503 (C1236T) genotypes were: 0.23 (CC), 0.41 (CT) and 0.36 (TT). For the rs2032582 (G2677T) SNP, genotype frequencies were 0.38 for GG, 0.45 for GT, 0.13 for TT, 0.03 for GA, and 0.01 for TA, whereas for rs1045642 (C3435T), genotype frequencies were 0.17 for CC, 0.5 for CT and 0.33 for TT. The observed distribution of the common variants in the Jordanian population was within the range detected in other populations. These data on MDR1 gene variants in the Jordanian population will be useful for investigations on response to P-glycoprotein substrate drugs.
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Affiliation(s)
- O F Khabour
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan.
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