1
|
Chen H, Lu D, Xiao Z, Li S, Zhang W, Luan X, Zhang W, Zheng G. Comprehensive applications of the artificial intelligence technology in new drug research and development. Health Inf Sci Syst 2024; 12:41. [PMID: 39130617 PMCID: PMC11310389 DOI: 10.1007/s13755-024-00300-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 07/27/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.
Collapse
Affiliation(s)
- Hongyu Chen
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Lu
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyi Xiao
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Shensuo Li
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luan
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weidong Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guangyong Zheng
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|
2
|
Li Y, Yang Y, Tong Z, Wang Y, Mi Q, Bai M, Liang G, Li B, Shu K. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Brief Bioinform 2024; 25:bbae172. [PMID: 38647153 PMCID: PMC11033846 DOI: 10.1093/bib/bbae172] [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: 12/10/2023] [Revised: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.
Collapse
Affiliation(s)
- Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yinqi Yang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Zhuohao Tong
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yu Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Qin Mi
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, P. R. China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, P. R. China
| | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| |
Collapse
|
3
|
Garana BB, Joly JH, Delfarah A, Hong H, Graham NA. Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing. BMC Bioinformatics 2023; 24:215. [PMID: 37226094 DOI: 10.1186/s12859-023-05343-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/16/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. RESULTS First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. CONCLUSIONS DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .
Collapse
Affiliation(s)
- Belinda B Garana
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA
| | - James H Joly
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA
- Nautilus Biotechnology, San Carlos, CA, USA
| | - Alireza Delfarah
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA
- Calico Life Sciences, South San Francisco, CA, USA
| | - Hyunjun Hong
- Department of Computer Science, Information Systems, and Applications, Los Angeles City College, Los Angeles, CA, USA
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA.
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
| |
Collapse
|
4
|
Rivas-Barragan D, Domingo-Fernández D, Gadiya Y, Healey D. Ensembles of knowledge graph embedding models improve predictions for drug discovery. Brief Bioinform 2022; 23:6831005. [PMID: 36384050 PMCID: PMC9677479 DOI: 10.1093/bib/bbac481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/04/2022] [Accepted: 10/08/2022] [Indexed: 11/18/2022] Open
Abstract
Recent advances in Knowledge Graphs (KGs) and Knowledge Graph Embedding Models (KGEMs) have led to their adoption in a broad range of fields and applications. The current publishing system in machine learning requires newly introduced KGEMs to achieve state-of-the-art performance, surpassing at least one benchmark in order to be published. Despite this, dozens of novel architectures are published every year, making it challenging for users, even within the field, to deduce the most suitable configuration for a given application. A typical biomedical application of KGEMs is drug-disease prediction in the context of drug discovery, in which a KGEM is trained to predict triples linking drugs and diseases. These predictions can be later tested in clinical trials following extensive experimental validation. However, given the infeasibility of evaluating each of these predictions and that only a minimal number of candidates can be experimentally tested, models that yield higher precision on the top prioritized triples are preferred. In this paper, we apply the concept of ensemble learning on KGEMs for drug discovery to assess whether combining the predictions of several models can lead to an overall improvement in predictive performance. First, we trained and benchmarked 10 KGEMs to predict drug-disease triples on two independent biomedical KGs designed for drug discovery. Following, we applied different ensemble methods that aggregate the predictions of these models by leveraging the distribution or the position of the predicted triple scores. We then demonstrate how the ensemble models can achieve better results than the original KGEMs by benchmarking the precision (i.e., number of true positives prioritized) of their top predictions. Lastly, we released the source code presented in this work at https://github.com/enveda/kgem-ensembles-in-drug-discovery.
Collapse
Affiliation(s)
| | - Daniel Domingo-Fernández
- Corresponding author: Daniel Domingo-Fernández, Department of Data Science. Enveda Biosciences, Boulder, CO, USA. E-mail:
| | | | | |
Collapse
|
5
|
Fusar-Poli P, Manchia M, Koutsouleris N, Leslie D, Woopen C, Calkins ME, Dunn M, Tourneau CL, Mannikko M, Mollema T, Oliver D, Rietschel M, Reininghaus EZ, Squassina A, Valmaggia L, Kessing LV, Vieta E, Correll CU, Arango C, Andreassen OA. Ethical considerations for precision psychiatry: A roadmap for research and clinical practice. Eur Neuropsychopharmacol 2022; 63:17-34. [PMID: 36041245 DOI: 10.1016/j.euroneuro.2022.08.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/04/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022]
Abstract
Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.
Collapse
Affiliation(s)
- Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | | | | | - Monica E Calkins
- Neurodevelopment and Psychosis Section and Lifespan Brain Institute of Penn/CHOP, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Michael Dunn
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore
| | - Christophe Le Tourneau
- Institut Curie, Department of Drug Development and Innovation (D3i), INSERM U900 Research unit, Paris-Saclay University, France
| | - Miia Mannikko
- European Federation of Associations of Families of People with Mental Illness (EUFAMI), Leuven, Belgium
| | - Tineke Mollema
- Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN), Brussels, Belgium
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Italy
| | - Lucia Valmaggia
- South London and Maudsley NHS Foundation Trust, London, UK; Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychiatry, KU Leuven, Belgium
| | - Lars Vedel Kessing
- Copenhagen Affective disorder Research Center (CADIC), Psychiatric Center Copenhagen, Denmark; Department of clinical Medicine, University of Copenhagen, Denmark
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA; Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Center for Psychiatric Neuroscience; The Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Gregorio Marañón; Health Research Institute (IiGSM), School of Medicine, Universidad Complutense de Madrid; Biomedical Research Center for Mental Health (CIBERSAM), Madrid, Spain
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | |
Collapse
|
6
|
Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
Collapse
Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| |
Collapse
|
7
|
Ratajczak F, Joblin M, Ringsquandl M, Hildebrandt M. Task-driven knowledge graph filtering improves prioritizing drugs for repurposing. BMC Bioinformatics 2022; 23:84. [PMID: 35246025 PMCID: PMC8894843 DOI: 10.1186/s12859-022-04608-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/09/2021] [Indexed: 02/07/2023] Open
Abstract
Background Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. Results We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Conclusion Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04608-y.
Collapse
Affiliation(s)
- Florin Ratajczak
- Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany. .,Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany.
| | | | | | | |
Collapse
|
8
|
Montagud A, Béal J, Tobalina L, Traynard P, Subramanian V, Szalai B, Alföldi R, Puskás L, Valencia A, Barillot E, Saez-Rodriguez J, Calzone L. Patient-specific Boolean models of signalling networks guide personalised treatments. eLife 2022; 11:e72626. [PMID: 35164900 PMCID: PMC9018074 DOI: 10.7554/elife.72626] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/27/2022] [Indexed: 11/22/2022] Open
Abstract
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.
Collapse
Affiliation(s)
- Arnau Montagud
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3BarcelonaSpain
| | - Jonas Béal
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
| | - Pauline Traynard
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| | - Vigneshwari Subramanian
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
| | - Bence Szalai
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
- Semmelweis University, Faculty of Medicine, Department of PhysiologyBudapestHungary
| | | | | | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3BarcelonaSpain
- ICREA, Pg. Lluís Companys 23BarcelonaSpain
| | - Emmanuel Barillot
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg UniversityHeidelbergGermany
| | - Laurence Calzone
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| |
Collapse
|
9
|
Domingo-Fernández D, Gadiya Y, Patel A, Mubeen S, Rivas-Barragan D, Diana CW, Misra BB, Healey D, Rokicki J, Colluru V. Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery. PLoS Comput Biol 2022; 18:e1009909. [PMID: 35213534 PMCID: PMC8906585 DOI: 10.1371/journal.pcbi.1009909] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/09/2022] [Accepted: 02/09/2022] [Indexed: 12/29/2022] Open
Abstract
Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.
Collapse
Affiliation(s)
| | - Yojana Gadiya
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Abhishek Patel
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Sarah Mubeen
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | | | - Chris W. Diana
- Enveda Biosciences, Boulder, Colorado, United States of America
| | | | - David Healey
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Joe Rokicki
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Viswa Colluru
- Enveda Biosciences, Boulder, Colorado, United States of America
| |
Collapse
|
10
|
Singla RK, Joon S, Shen L, Shen B. Translational Informatics for Natural Products as Antidepressant Agents. Front Cell Dev Biol 2022; 9:738838. [PMID: 35127696 PMCID: PMC8811306 DOI: 10.3389/fcell.2021.738838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.
Collapse
Affiliation(s)
- Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
11
|
Lecca P. Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge. FRONTIERS IN BIOINFORMATICS 2021; 1:746712. [PMID: 36303798 PMCID: PMC9581010 DOI: 10.3389/fbinf.2021.746712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/08/2021] [Indexed: 11/13/2022] Open
Abstract
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.
Collapse
Affiliation(s)
- Paola Lecca
- Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani, Bolzano, Italy
| |
Collapse
|
12
|
Figueiredo RQ, Raschka T, Kodamullil AT, Hofmann-Apitius M, Mubeen S, Domingo-Fernández D. Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms. Nucleic Acids Res 2021; 49:7939-7953. [PMID: 34197603 PMCID: PMC8373148 DOI: 10.1093/nar/gkab556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/17/2021] [Accepted: 06/11/2021] [Indexed: 12/17/2022] Open
Abstract
We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.
Collapse
Affiliation(s)
- Rebeca Queiroz Figueiredo
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany.,Fraunhofer Center for Machine Learning, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala, India
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany.,Fraunhofer Center for Machine Learning, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Fraunhofer Center for Machine Learning, Germany.,Enveda Biosciences, Boulder, CO 80301, USA
| |
Collapse
|
13
|
Singh N, Villoutreix BO. Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises. Comput Struct Biotechnol J 2021; 19:2537-2548. [PMID: 33936562 PMCID: PMC8074526 DOI: 10.1016/j.csbj.2021.04.059] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 12/11/2022] Open
Abstract
There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.
Collapse
Affiliation(s)
- Natesh Singh
- Université de Paris, Inserm UMR 1141 NeuroDiderot, Robert-Debré Hospital, 75019 Paris, France
| | - Bruno O. Villoutreix
- Université de Paris, Inserm UMR 1141 NeuroDiderot, Robert-Debré Hospital, 75019 Paris, France
| |
Collapse
|