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Computational Drug Discovery: Machine Learning for Making Sense of Big Data in Drug Discovery

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Computational Drug Discovery: Machine Learning for Making Sense of Big Data in Drug Discovery

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In this lecture, I provide an overview on how computers can be instrumental in drug discovery efforts. Topics covered includes: big data as a result of omics effort; bioinformatics; cheminformatics; biological space; chemical space; how computers particularly machine learning (and data science) can be applied in the context of drug discovery.

A video of this lecture is also provided on the "Data Professor" YouTube channel available at http://bit.ly/dataprofessor

If you are fascinated about data science, it would mean the world to me if you would consider subscribing to this channel (by clicking the link below):
http://bit.ly/dataprofessor

In this lecture, I provide an overview on how computers can be instrumental in drug discovery efforts. Topics covered includes: big data as a result of omics effort; bioinformatics; cheminformatics; biological space; chemical space; how computers particularly machine learning (and data science) can be applied in the context of drug discovery.

A video of this lecture is also provided on the "Data Professor" YouTube channel available at http://bit.ly/dataprofessor

If you are fascinated about data science, it would mean the world to me if you would consider subscribing to this channel (by clicking the link below):
http://bit.ly/dataprofessor

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Computational Drug Discovery: Machine Learning for Making Sense of Big Data in Drug Discovery

  1. 1. Computational Drug Discovery Associate Professor Dr. Chanin Nantasenamat 
 E-mail: chanin.nan@mahidol.edu YouTube: http://bit.ly/dataprofessor Machine Learning for Making Sense of Big Data in Drug Discovery
  2. 2. About the Speaker • Research group website at http://codes.bio • Codes and Data at http://github.com/ chaninn and http://github.com/chaninlab • YouTube Channel called Data Professor available at http://bit.ly/dataprofessor • Data Professor FaceBook Page at 
 http://facebook.com/dataprofessor Icon made by Freepik from www.flaticon.com
  3. 3. Disease • The word ‘disease’ is defined by Cambridge Dictionary as
 
 illness of people, animals, plants, etc., caused by infection or a failure of health rather than by an accident http://static.filmannex.com/users/galleries/ 294182/19265_fa_rszd.jpg
  4. 4. Drugs • A ‘drug’ is a biological or chemical entity that can modulate the course of a disease state by interacting with its target protein • Biological entity
 (e.g. antibodies) • Chemical entity
 (e.g. small molecules) Natthapon Ngamnithiporn. Image from FreePik.
 http://www.freepik.com/free-photo/packings-of-pills-and- capsules-of-medicines_1178867.htm
  5. 5. Li et al. BMC Syst Biol 8 (2014) 141.
  6. 6. Drug Discovery Process • Costs ~2 billion USD • Takes about 10-15 years • Failure rate is > 90% http://drugdiscovery.nd.edu/
  7. 7. Drug Discovery Process Ashburn andThor. Nature Rev. Drug Discov. 3 (2004) 673-683 Identify target protein that is key in modulating disease Screen for ‘hit’ molecules that can inhibit the target protein ‘Hit-to-lead’ and ‘Lead optimization’ Evaluate pharmaco- kinetic properties Initiate Clinical trials to evaluate safety & dosage; efficacy & side effects; adverse reaction to long-term use Drug reaches the market
  8. 8. https://slideplayer.com/slide/13182763/ From a million to one
  9. 9. Multi-objective optimization • A drug need not only target the protein of interest but must also possess other properties • Desirable characteristics of a drug: 1. Binds selectively to the target protein 2. Absorbs in the stomach (oral drugs) 3. Permeates gut-wall or cell-wall (can reach target site) 4. Metabolically stable 5. Non-toxic 6. Can be synthesized • To achieve all these desirable properties, the chemical structure will need to be optimized (an optimal balance will need to be achieved against many factors)
  10. 10. Creating new compounds • We can look to nature for inspiration (biologically inspired) or use existing drugs as starting point • Medicinal chemists optimize existing componds by modifying them in a process known as bioisosteric replacement (replacing a hydrogen atom by a halogen atom) • Cheminformaticians can computationally enumerate a compound (compound enumeration) library using the rules of organic chemistry (considers chemical stability and synthetic feasibility) Icon made by dDara from www.flaticon.com
  11. 11. Molecules • Molecules can be thought of as framework of atoms (molecular graph) where atoms are vertices and bonds are edges - Each vertices can typically be one of nine atoms (C, N, O, F, P, S, Cl or Br) - Each edge that links the vertices can be a single, double or triple bond • Compound enumeration as performed by the research group of JL Reymond (Acc Chem Res 2015, 48(3):722-730) - Molecules of up to 13 atoms ⟶ 977 million possible molecules (109) - Molecules of up to 17 atoms ⟶ 166 billion possible molecules (1011)
  12. 12. Chemical space • Theoretically possible chemical space as revealed via compound enumeration by the research group of JL Reymond (Acc Chem Res 2015, 48(3):722-730) - Molecules of up to 13 atoms ⟶ 977 million possible molecules (109) - Molecules of up to 17 atoms ⟶ 166 billion possible molecules (1011) • Drug space (<500 Da) is estimated to constitute up to 40 atoms (in some cases, even more) ⟶ roughly 1060 molecules
  13. 13. Drug Discovery Toolbox Combina( torial, Chemistry, Chemical, Libraries, Chemical, Space, High( Throughput, Screening, Property, Filters, Compu( ta;onal, Chemistry, Machine( Learning( QSAR( Proteo3 chemo3 metrics( Molecular( Modeling( Molecular( Dynamics( Molecular( Docking(
  14. 14. Bioactivity • Bioactivity is the activity elicited by the target protein of interest • Such target proteins are typically involved in key pathways that influence the course of a disease • Thus, great attention has been placed to modulate these target proteins • Primary literature • Curated Databases • ChEMBL, BindingDB, MOAD, PubChem • Open Innovation • Pharmaceutical companies are making data publicly available for non- commercial diseases
  15. 15. What can computers do? • Computers (IBM Deep Blue) have defeated human in Jeopardy and Chess • Google released a self-driving car • NASA uses computers to simulate space missions • Computers are being used to design aircrafts and cars • Supermarkets and Shopping Malls are using our purchase history to analyze and predict our spending behavior • Why not use it to discover, design and develop new drugs? • Computers (deep learning) can paint likeVan Gogh and Picasso • Computers can programmatically code music (Sonic Pi) • Computers can dream
  16. 16. http://www.boredpanda.com/computer-deep-learning-algorithm-painting-masters/
  17. 17. https://storage.googleapis.com/cdn.thenewstack.io/media/2015/07/google-deep-dream- artificial-neural-networks-12.jpg
  18. 18. Why do we need computational models in drug discovery? • To discern structure-activity relationship of chemical library • In vitro data are limited, expensive, time-consuming, laborious, etc. • Computational models can be quickly built to preliminarily predict the pharmacokinetics and bioactivity of query compounds Anuwongcharoen et al. PeerJ 4 (2016) e1958
  19. 19. Questions that can be answered by computational models • What target proteins could my compound(s) bind to and modulate? • Would my compound bind unspecifically to other proteins and thus have off-target activity? • What type of compounds can bind and modulate the bioactivity of the target protein of my interest? • Are there similar compounds to my query compound that may potentially exert similar binding behavior? • How does my compound bind to the protein structure of its target? Hall et al. Prog Biophys Mol Biol 116 (2014) 82-91. • How can I modify the structure of my compound to enhance its pharmacokinetics and bioactivity?
  20. 20. ADMET QSAR Pharmacophore Statistical molecular design Molecular modeling Protein structure prediction - Homology/comparative - Ab initio Molecular dynamics Normal mode analysis Docking/reverse docking Binding cavity analysis Pharmacophore Protein–ligand interactome Protein–protein interactome Drug target gene expression Intrinsically disordered proteins Allo-network drugs High-throughput synthesis High-throughput screening Privileged structures Bioisostere Chemoisostere Scaffold hopping Sequence alignment BLAST Phylogenetic analysis Biological space Computational chemistry Molecular descriptors Chemical space Profiling Filtering - Lipinski’s rule of 5 Search - Molecular similarity - Substructure similarity - Shape, volume and charge-based similarityDatabases Small molecules - DrugBank - ChEMBL - Pubchem - BindingDB - ZINC Proteins - PDB - UniProt - SCOP Protein-protein - MINT - STITCH - STRING Pathway - KEGG - Reactome Proteochemometrics Computational chemogenomics Graph/network theory Fragment-based docking Fragment-based QSAR Ligand growing Structure-based Systems-based Medicinal chemistry Bioinformatics Cheminformatics Ligand-based Chemogenomics Fragment-based Maximizing computational tools for successful drug discovery Overview of Computational Drug Discovery Nantasenamat and Prachayasittikul. Expert Opin Drug Discov 10 (2015) 321-329.
  21. 21. Bioinformatics • Bioinformatics is a discipline entailing the use of computational approaches to analyze biological data ‣ Analyze and compare genes, proteins and genomes ‣ Explore structures and functions of biomolecules (DNA, protein, lipid and carbohydrate) ‣ Explore network biology and metabolic pathways http://www.gettyimages.com/detail/photo/bioinformatics-background-concept-royalty-free- image/475811932?esource=SEO_GIS_CDN_Redirect I424 L428 F404 R394 E353 A350 D351 L354 P535 W383L525 Suvannang et al. Manuscript under Preparation.
  22. 22. • Cheminformatics is a discipline at the interface of chemistry and computers that enables the analysis of various aspects relevant to chemical structures ‣ Chemical space for investigating Molecular similarity/diversity ‣ Molecular descriptors (e.g. MW, LogP, nHBdon, nHBacc) and Quantum chemical descriptors (HOMO, LUMO, HOMO-LUMO) Cheminformatics Ertl and Rohde. J Cheminf 4 (2012) 12.
  23. 23. Drugs and its pre-cursors • Fragments - are one of many substructures found in a compound (drug) • Privileged substructures - are substructures that are commonly found as inhibitors/activators (drugs) against several therapeutic targets • Hits - are a small subset of compounds from large chemical libraries that are identified from high-throughput screening • Leads - are compounds that have undergone minor structural optimization from hits. From there, these leads often undergo further rounds of “lead optimization” • Drugs - are one of many leads that had passed rigorous tests (pre-clinical and clinical trials) before reaching the market
  24. 24. Identifying hits • So how does one go about identifying hit compounds? - High-throughput screening 
 (Experimental and computational) - Find similar compounds to known actives as the bioactivity of each compound is not an isolated point (similar chemical structures also provide similar biological activity) ๏ 30% of these similar compounds to known actives, are themselves actives https://southernresearch.org/news/nih-contract-high- throughput-screening-for-zika/ Hernandex-Santoyo et al. Protein-protein and protein- ligand docking. DOI:10.5772/56376 MartinYC, J Med Chem 2002, 45(19):4350-4358
  25. 25. Lead generation (Hit-to-Lead) • Identified hits from high- throughput screens are transformed to leads by means of limited structural modification (as to optimize their ADMET properties) • Generated leads are subjected to further rounds of lead optimization Fuller N et al. Drug DiscovToday 2016, 21(8):1272-1283.
  26. 26. Fragment-based Drug Design Source: http://practicalfragments.blogspot.com/2011/08/first-fragment-based-drug-approved.html Zelboraf treats melanoma by inhibiting BRAF.
  27. 27. DeLaBarre B. http://consultingbiochemist.com/2014/12/cone-chemical-space/
  28. 28. • Christopher Lipinski analyzed a large set of > 2,000 orally-active drugs that led to what is known as the Lipinski’s Rule of 5, which is a set of rules defining the drug like-ness of small molecules ‣ Molecular weight < 500 Da ‣ Lipophilicity (LogP) < 5 ‣ Hydrogen bond donors < 5 ‣ Hydrogen bond acceptors < 10 Lipinski’s Rule of 5 a b c da b c d Christopher Lipinski @ Pfizer Lipinski et al.Adv Drug Deliv Rev 23 (1997) 3-25 Suvannang et al. (2017) Unpublished results
  29. 29. • In drug discovery, there is a tendency for the lipophilicity and molecular weight to increase as lead optimization progresses as to improve the drug’s affinity and selectivity ‣ Molecular weight < 300 Da ‣ Lipophilicity (LogP) < 3 ‣ Hydrogen bond donors < 3 ‣ Hydrogen bond acceptors < 3 ‣ Rotatable bonds < 3 Lead-like Rule of 3
  30. 30. Chemical space • Chemical space can be generally defined as the universe of synthetically feasible small molecules of <500 Da that is estimated to be in the order of ~1060 molecules • The visualization of which gives us a bird’s eye glance at the relative diversity/likeness of chemical libraries • Reymond group at University of Bern, Switzerland developed a computational algorithm that enumerates all possible chemical structures that can be built from 17 heavy atoms in their GDB-17 database which amounts to 166.4 billion Reymond and Awale.ACS Chem Neurosci 3 (2012) 649-657.
  31. 31. Biological space • Biological space refers to the chemical space of druggable protein families ‣ ADMET ‣ Aminergic/Lipophilic GPCR space ‣ Kinase space ‣ Protease space ‣ CYP450 ‣ Nuclear receptors Petit-Zeman S. http://www.nature.com/horizon/ chemicalspace/background/figs/explore_b1.html
  32. 32. Fragment space • Fragment space can be defined as the universe or collection of all possible molecular fragments (substructures) • Fragments are < 300 Da • Utilization of the fragment space has been suggested to allow more diverse exploration of the possible chemical space • Reymond group also extracted 10 million fragments from the GDB-17 https://software.zbh.uni-hamburg.de/assets/softwareserverslide6- a0e42ecb3651120926821932574540d5b2e83ff0209654f9ab14 804c7858451a.png Virshup et al. J Am Chem Soc 135 (2013) 7296-7303
  33. 33. Koch et al. PNAS 102 (2005) 17272-17277 Structural classification of natural products (SCONP)
  34. 34. Nadin et al.Angew Chem Int Ed 51 (2012) 1114-1122.
  35. 35. Polypharmacology • There is a paradigm shift from ‘one drug-one target’ to ‘one drug- multiple targets’ • Unintended off-target binding may elicit undesirable side effects and adverse effects • Desirable off-target binding gives you drug repositioning opportunities • Knowledge of polypharmacology may aid in the design of multi-targeted drugs Reddy and Zhang. Expert Rev Clin Pharmacol 6 (2013) 41-47 Kinase targets of Staurosporine
  36. 36. Drug repositioning/repurposing • There is a need to discover new drugs for treatment especially rare and neglected diseases • Drug repositioning/ re- purposing is a lucrative approach as it tests existing FDA-approved drugs against various other whole-cell and target assays Wu et al. Mol BioSyst 9 (2013) 1268-1281.
  37. 37. Experimental activity (pIC50) 5.0 5.5 6.0 6.5 7.0 7.5 8.0 Predictedactivity(pIC50) 5.0 5.5 6.0 6.5 7.0 7.5 8.0 What is QSAR? (1) • QSAR/QSPR is the acronym of Quantitative Structure-Activity/Property Relationship • QSAR seeks to correlate structural features of compounds with their biological activities
  38. 38. What is QSAR? (2) • Structure governs activity/ property • Typically in the medicinal chemistry literature, effects of substituent groups on activity is extensively studied 1" 2" 3" 4" 5" 6" • QSAR/QSPR studies exploits this knowledge for modeling the biological or chemical activities/properties
  39. 39. What is QSAR? (3) • QSAR involves three main concepts: 1. Selecting a biological activity or chemical property of interest 2. Generating the physicochemical description 3. Predicting the biological activity or chemical property Qm# Energy# μ# HOMO# LUMO# HOMO0LUMO#gap# 0.2271& '309.834& 1.0521& '0.21346& '0.0127& 0.20076& 0.2142& '195.31& 0.2337& '0.22611& '0.01915& 0.20696& IC50% 0.05$ 1.50$ Molecular Descriptors Biological Activity Computational Chemistry Machine Learning Compounds of Interest Predict
  40. 40. Growth of QSAR? • A search in SCOPUS shows the growing trend of QSAR publications
  41. 41. Data set preparation QSAR modeling ChEMBL 23 Bioactivity measured by IC50 Remove duplicate SMILES Bioactivity data of ER α inhibitors Initial data set 10,666 bioactivity data for 5,809 compounds IC50 subset 3,527 compounds Final data set 1,299 compounds Select entries with CONFIDENCE_SCORE=9 and assay_type=B Selected data set 1,346 compounds Mechanistic interpretation of feature importance Feature selection 12 sets of PaDEL fingerprints Descriptor calculation Data splitting Evaluate performance QSAR model Predicted pIC50 values Y-scrambling for evaluating chance correlation Delete entries with < or > signs and those with Salt removal Transform IC50 to pIC50 Final data set Tautomer standardization Remove collinear descriptors 70/30 split ratio Perform 10 data splits Delete entries with missing SMILES notation R2,Q2, Rm 2, RMSE A typical QSAR workflow Suvannang et al. RSC Adv 2018, 8: 11344-11356
  42. 42. Applications of QSAR/QSPR models • Regulatory Use: QSAR for modelling environmental toxicity/chemical hazards by EPA and OECD • Drug Design: QSAR for modelling biological activities • Materials Design: QSPR for modelling chemical properties
  43. 43. GFP$ LPS$ QSAR$ DNA$ PCP$ BPA$ Bacitracin$ Quorum$ Furin$ Vasorelaxant$ Vitamin$E$ Template?$ Monomer$ Phenol$ Sulfonamide$ EDTA?$ DPPC$ Copper$ Complex$ AnDmalarial$ AnD?H1N1$ Aromatase$ Inhibitors$ CYP450$ Inhibitors$ Monte$Carlo$ Feature$$ SelecDon$ Text$ Mining$
  44. 44. Biological activity/chemical property modeled by QSAR Biological Activity Chemical Property Bioconcentration Boiling point Biodegradation Chromatographic retention time Carcinogenicity Dielectric constant Drug metabolism Diffusion coefficient Inhibitor constant Dissociation constant Mutagenicity Melting point Permeability Reactivity Blood brain barrier Solubility Skin Stability Pharmacokinetics Thermodynamic properties Receptor binding Viscosity Nantasenamat et al. EXCLI J. (2009) 8: 74-88
  45. 45. Multiple Compounds Single 
 Target Protein Multiple Compounds Multiple 
 Target Proteins QSAR Proteochemometrics
  46. 46. Summary • QSAR models allow us to understand how changes to the chromophore structure leads to GFP color change • PCM models allow us to understand how changes to chromophore structure, changes to protein structure and the chromophore-protein interaction influences GFP color change • Insights from the predictive models could be used in further extending the spectral repertoire of GFP Nantasenamat C et al. J. Comput. Chem. 35(27): 1951-1966.
  47. 47. Proteochemometrics • Proteochemometrics was developed by Maris Lapins and Jarl Wikberg of Uppsala University in 2001 • Advantages • Can explain ligand-target affinity by providing detailed maps down to the substructures and amino acid level • Can be used to rationalise why a ligand is active toward one target and not on the other related target • Has been shown to be useful for Drug Repositioning • Could be used for Personalized Medicine
  48. 48. Conclusion (1) • It is without a doubt that the QSAR paradigm boasts much benefit for the rational design of robust compounds • Nevertheless, there are certain shortcomings that may limit the widespread application of QSAR • Workflow of QSAR model development • High dimensionality of the input space • Representation of the molecular structure • Interpretability and meaning of the developed QSAR models • Presence of outliers or activity cliffs • Validation of QSAR model performance • Applicability in real-world setting
  49. 49. Conclusion (2) • In spite of certain inherent flaws, the QSAR paradigms inevitably one of the most useful forces contributing to the rapid development of drug discovery and design. • As with all technologies, QSAR is not perfect; however, its weaknesses and flaws are continuously being identified, solved and reformed to help shape a new improved and robust approach that is approaching minimal predictive error • To help realize the goal of developing an intuitive approach toward the development of robust QSAR models, our laboratory had developed a software that affords a semi- automated if not automated QSAR modeling.
  50. 50. Conclusion (3) • At more than 10 years of QSAR research, we can say that the demise of QSAR is a myth if done properly and we had only scratched the surface of its full potential • QSAR is continuously evolving…starting from 2D-QSAR to 
 8D-QSAR! • Proteochemometrics (so to say Multi-Target QSAR) enables us to take advantage of the explosion of Omics data
  51. 51. A"so%ware"for"performing"automated"Data"Mining" AutoWeka"is"a" Python"wrapper" of"Weka" • It is freely available at http://www.mt.mahidol.ac.th/autoweka/ • Nantasenamat et al. Chapter 8:AutoWeka:Toward an Automated Data Mining Software for QSAR and QSPR Studies. In: Cartwright H.Artificial Neural Networks, Springer, pp. 119-147. AutoWeka
  52. 52. BioCurator Nantasenamat et al. Manuscript under preparation. • We had developed a web application that allow users to upload ChEMBL bioactivity data for automatic data curation Protocol • The web app selects a subset of IC50/Ki data • Removes redundant compounds if bioactivity values exceed 2 SD • Remove data with < or > symbols in the bioactivity label • Remove redundant compounds based on SMILES notation
  53. 53. osFP Simeon et al. J Cheminf 8 (2016) 72. Protocol • The web app accepts the input peptide sequence and computes amino acid composition descriptors • Applies the constructed predictive model to predict the class label of the query peptide • Predicted class label is relayed into the Results output Simeon et al. J Cheminform (2016) 8:72 DOI 10.1186/s13321-016-0185-8 RESEARCH ARTICLE osFP: a web server for predicting the oligomeric states of fluorescent proteins Saw Simeon1 , Watshara Shoombuatong1 , Nuttapat Anuwongcharoen1 , Likit Preeyanon2 , Virapong Prachayasittikul2 , Jarl E. S. Wikberg3 and Chanin Nantasenamat1* Abstract Background: Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of Open Access
  54. 54. HemoPred Win et al. Future Med Chem 9 (2017) 275-291. Protocol • The web app accepts the input peptide sequence and computes amino acid composition descriptors • Applies the constructed predictive model to predict the class label of the query peptide • Predicted class label is relayed into the Results output Future Medicinal Chemistry Research Article HemoPred: a web server for predicting the hemolytic activity of peptides For reprint orders, please contact reprints@future-science.com
  55. 55. CryoProtect Win et al. Future Med Chem 9 (2017) 275-291. Protocol • The web app accepts the input peptide sequence and computes amino acid composition descriptors • Applies the constructed predictive model to predict the class label of the query peptide • Predicted class label is relayed into the Results output Research Article CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins Reny Pratiwi,1,2 Aijaz Ahmad Malik,1 Nalini Schaduangrat,1 Virapong Prachayasittikul,3 Jarl E. S. Wikberg,4 Chanin Nantasenamat,1 and Watshara Shoombuatong1 1 Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand 2 Department of Medical Laboratory Technology, Faculty of Health Science, Setia Budi University, Surakarta 57127, Indonesia 3 Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand 4 Hindawi Journal of Chemistry Volume 2017,Article ID 9861752, 15 pages https://doi.org/10.1155/2017/9861752
  56. 56. How to get started in CDD? • Hardware • Laptop • Desktop • High- performance computer • Compute clusters • Cloud computing • Software • Commercial • Free • Programming • C, Java, etc. • R, Python, MATLAB, etc.
  57. 57. Computational Drug Discovery based on Open Source • Data source ◦ Bioactivity data: ChEMBL, PubChem, BindingDB ◦ Chemical database: ZINC, ChemSpider, GDB-17 ◦ Biological database: PDB, UniProt • Data curation and pre-processing ◦ BioCurator (developed in-house) ◦ Babel • Descriptor calculation ◦ Rcpi, PyDPI, CDK, PADEL • Multivariate analysis ◦ R: caret ◦ Python: scikit-learn • Plots ◦ R: ggplot ◦ Python: MatPlotLib, seaborn Molecular modeling ◦ Avogadro ◦ PyMol ◦ Chimera ◦ VMD • Molecular docking ◦ AutoDock • Molecular dynamics ◦ Gromacs ◦ NAMD

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