Aplicaciones de la inteligencia artificial en la farmacología básica y clínica

Autores/as

  • Christos Matsingos Universidad de los Andes
  • Ana María Urdaneta AIpocrates
  • Juan Camilo Hernández Universidad CES, Medellín
  • Ricardo A. Peña Silva Universidad de los Andes

DOI:

https://doi.org/10.56050/01205498.1652

Palabras clave:

Farmacia, análisis de datos, eficacia, inteligencia artificial, medicamentos

Resumen

El descubrimiento y desarrollo de fármacos es un proceso complicado y arduo que implica un gran esfuerzo interdisciplinar. A grandes rasgos, el proceso puede dividirse en dos partes: la preclínica y la clínica, que juntas pueden durar hasta 12 años y costar entre 2 y 3 billones de dólares. Debido a la compleja naturaleza de la creación de nuevos medicamentos, que implica consideraciones bioquímicas y fisicoquímicas, así como consideraciones de seguridad y eficacia clínica, el proceso de descubrimiento de fármacos se caracteriza por una alta tasa de fracasos. En la era de la información, este proceso de desarrollo suele estar asociado a la generación de grandes cantidades de datos. La inteligencia artificial ha permitido aprovechar estos datos para acelerar el proceso de descubrimiento de fármacos y evitar posibles escollos que puedan llevar al fracaso de la comercialización de un medicamento. En esta revisión analizamos los nuevos avances en inteligencia artificial y aprendizaje automático en diferentes partes del proceso de descubrimiento de fármacos, desde la síntesis química hasta la selección de candidatos para los ensayos clínicos. Se muestra que la inteligencia artificial se ha aplicado en todas las etapas del descubrimiento de fármacos y se ha utilizado en gran medida para revolucionar los métodos de investigación tradicionales. La inteligencia artificial no sólo se ha utilizado para facilitar y acelerar los procesos de descubrimiento, sino también para obtener conocimientos y detectar patrones que no se conocían antes. El uso de inteligencia artificial es indispensable para el futuro del descubrimiento de fármacos.

Biografía del autor/a

Christos Matsingos, Universidad de los Andes

MSc. Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia.
School of Physical and Chemical Sciences, Queen Mary University of London, Londres, Reino Unido.

Ana María Urdaneta, AIpocrates

MD. MSc. Miembro fundador de AIpocrates, Comité de analítica y calidad del dato.

Juan Camilo Hernández, Universidad CES, Medellín

QF. MSc. Universidad CES, Medellín, Colombia.

Ricardo A. Peña Silva, Universidad de los Andes

MSc. Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia.

MD, PhD. Lown Scholars Program, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Referencias bibliográficas

1. Zhavoronkov A, Vanhaelen Q, Oprea TI. Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology? Clin Pharmacol Ther. 2020;107 (4):780-5.
2. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26 (1):80-93.
3. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev. 2019;119 (18):10520-94.
4. Chan HCS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol Sci. 2019;40 (10):801.
5. Silcox C. La inteligencia artificial en el sector salud. Banco Interamericano de Desarrollo; 2020.
6. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18 (6):463-77.
7. Koromina M, Pandi MT, Patrinos GP. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. OMICS. 2019;23 (11):539-48.
8. Savage N. Tapping into the drug discovery potential of AI. Biopharma Dealmakers [Internet]. 2021. Available from: https://www.nature.com/articles/d43747-021-00045-7.
9. Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annu Rev Pharmacol Toxicol. 2020;60:573-89.
10. Cavasotto CN, Phatak SS. Homology modeling in drug discovery: current trends and applications. Drug Discov Today. 2009 Jul;14(13-14):676-83. doi:10.1016/j.drudis.2009.04.006. Epub 2009 May 5.
11. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020 Jan;577(7792):706-710. doi: 10.1038/s41586-019-1923-7. Epub 2020 Jan 15.
12. Hayik SA, Dunbrack R Jr, Merz KM Jr. A Mixed QM/MM Scoring Function to Predict Protein-Ligand Binding Affinity. J Chem Theory Comput. 2010 Sep 1;6(10):3079-3091. doi: 10.1021/ct100315g.
13. Wang M, Mei Y, Ryde U. Predicting Relative Binding Affinity Using Nonequilibrium QM/MM Simulations. J Chem Theory Comput. 2018 Dec 11;14(12):6613-6622. doi:10.1021/acs.jctc.8b00685. Epub 2018 Nov 8.
14. Zhang YJ, Khorshidi A, Kastlunger G, Peterson AA. The potential for machine learning in hybrid QM/MM calculations. J Chem Phys. 2018 Jun 28;148(24):241740. doi: 10.1063/1.5029879.
15. Sittampalam GS, Kahl SD, Janzen WP. High-throughput screening: advances in assay technologies. Curr Opin Chem Biol. 1997 Oct;1(3):384-91. doi: 10.1016/s1367- 5931(97)80078-6.
16. Ahuja A, Al-Zogbi L, Krieger A. Application of noisereduction techniques to machine learning algorithms for breast cancer tumor identification. Comput Biol Med. 2021 Aug;135:104576. doi: 10.1016/j.compbiomed.2021.104576. Epub 2021 Jun 19.
17. Nitta N, Sugimura T, Isozaki A, Mikami H, Hiraki K, Sakuma S, et al. Intelligent Image-Activated Cell Sorting. Cell. 2018 Sep 20;175(1):266-276.e13. doi: 10.1016/j. cell.2018.08.028. Epub 2018 Aug 27.
18. Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018 Mar 28;555(7698):604-610. doi: 10.1038/ nature25978.
19. Coley CW, Green WH, Jensen KF. Machine Learning in Computer-Aided Synthesis Planning. Acc Chem Res. 2018 May 15;51(5):1281-1289. doi: 10.1021/acs. accounts.8b00087. Epub 2018 May 1.
20. Maryasin B, Marquetand P, Maulide N. Machine Learning for Organic Synthesis: Are Robots Replacing Chemists? Angew Chem Int Ed Engl. 2018 Jun 11;57(24):6978- 6980. doi: 10.1002/anie.201803562. Epub 2018 Apr 27.
21. Wei JN, Duvenaud D, Aspuru-Guzik A. Neural Networks for the Prediction of Organic Chemistry Reactions. ACS Cent Sci. 2016 Oct 26;2(10):725-732. doi: 10.1021/acscentsci.6b00219. Epub 2016 Oct 14.
22. Ahneman DT, Estrada JG, Lin S, Dreher SD, Doyle AG. Predicting reaction performance in C-N crosscoupling using machine learning. Science. 2018 Apr 13;360(6385):186-190. doi: 10.1126/science.aar5169. Epub 2018 Feb 15. Erratum in: Science. 2018 Apr 13;360(6385).
23. Coley CW, Barzilay R, Jaakkola TS, Green WH, Jensen KF. Prediction of Organic Reaction Outcomes Using Machine Learning. ACS Cent Sci. 2017 May 24;3(5):434-443. doi: 10.1021/acscentsci.7b00064. Epub 2017 Apr 18.
24. Zhou Z, Li X, Zare RN. Optimizing Chemical Reactions with Deep Reinforcement Learning. ACS Cent Sci. 2017 Dec 27;3(12):1337-1344. doi: 10.1021/acscentsci.7b00492. Epub 2017 Dec 15.
25. Caramelli D, Salley D, Henson A, Camarasa GA, Sharabi S, Keenan G, et al. Networking chemical robots for reaction multitasking. Nat Commun. 2018 Aug 24;9(1):3406. doi: 10.1038/s41467-018-05828-8.
26. Granda JM, Donina L, Dragone V, Long DL, Cronin L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018 Jul;559(7714):377-381. doi: 10.1038/s41586-018-0307-8. Epub 2018 Jul 18.
27. Wollenberg A, Flohr C, Simon D, Cork MJ, Thyssen JP, Bieber T, et al. European Task Force on Atopic Dermatitis statement on severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection and atopic dermatitis. J Eur Acad Dermatol Venereol. 2020;34 (6):e241-e2.
28. Perera D, Tucker JW, Brahmbhatt S, Helal CJ, Chong A, Farrell W, et al. A platform for automated nanomolescale reaction screening and micromole-scale synthesis in flow. Science. 2018 Jan 26;359(6374):429-434. doi: 10.1126/science.aap9112.
29. Tyrchan C, Evertsson E. Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations. Comput Struct Biotechnol J. 2016 Dec 13;15:86-90. doi:10.1016/j.csbj.2016.12.003.PMID: 28066532; PMCID: PMC5198793.
30. Sheridan RP, Wang WM, Liaw A, Ma J, Gifford EM. Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. J Chem Inf Model. 2016 Dec 27;56(12):2353-2360. doi: 10.1021/acs.jcim.6b00591. Epub 2016 Dec 13.
31. Sheridan RP, Wang WM, Liaw A, Ma J, Gifford EM. Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. Journal of Chemical Information and Modeling2016. p. 2353-60.
32. Wallach I, Dzamba M, Heifets A. AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery. 2015. p. 1-11.
33. Sampaio MF, Hirata MH, Hirata RD, Santos FC, Picciotti R, Luchessi AD, et al. AMI is associated with polymorphisms in the NOS3 and FGB but not in PAI-1 genes in young adults. Clin Chim Acta. 2007;377 (1-2):154-62.
34. Mao F, Kong Q, Ni W, Xu X, Ling D, Lu Z, et al. Melting Point Distribution Analysis of Globally Approved and Discontinued Drugs: A Research for Improving the Chance of Success of Drug Design and Discovery. ChemistryOpen. 2016 Mar 21;5(4):357-68. doi: 10.1002/open.201600015.
35. Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018 Jul 27;361(6400):360-365. doi: 10.1126/science.aat2663. Epub 2018 Jul 26.
36. Shen J, Nicolaou CA. Molecular property prediction: recent trends in the era of artificial intelligence. Drug Discov Today Technol. 2019 Dec;32-33:29-36. doi: 0.1016/j.ddtec.2020.05.001. Epub 2020 Jul 1.
37. Sivaraman G, Jackson NE, Sanchez-Lengeling B, Vázquez-Mayagoitia Á, Aspuru-Guzik A, Vishwanath V, et al. A diversified machine learning strategy for predicting and understanding molecular melting points. ChemRxiv2019. p. 1-42.
38. Lenselink EB, Stouten PFW. Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge. J Comput Aided Mol Des. 2021 Aug;35(8):901-909. doi: 10.1007/s10822-021-00405-6. Epub 2021 Jul 17.
39. Puzyn T, Leszczynska D, Leszczynski J. Toward the development of "nano-QSARs": advances and challenges. Small. 2009 Nov;5(22):2494-509. doi: 10.1002/smll.200900179.
40. Li S, Zhai S, Liu Y, Zhou H, Wu J, Jiao Q, et al. Experimental modulation and computational model of nanohydrophobicity. Biomaterials. 2015 Jun;52:312-7. doi: 10.1016/j.biomaterials.2015.02.043. Epub 2015 Feb 28.
41. Krajišnik D, Stepanović-Petrović R, Tomić M, Micov A, Ibrić S, Milić J. Application of artificial neural networks in prediction of diclofenac sodium release from drug-modified zeolites physical mixtures and antiedematous activity assessment. J Pharm Sci. 2014 Apr;103(4):1085-94. doi: 10.1002/jps.23869. Epub 2014 Feb 4.
42. Drăgoi EN, Curteanu S, Fissore D. On the Use of Artificial Neural Networks to Monitor a Pharmaceutical Freeze-Drying Process. Drying Technology 31(1):72-81.
43. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3 (8):673-83.
44. Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012;11 (3):191-200.
45. Pammolli F, Magazzini L, Riccaboni M. The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov. 2015 Jul;14(7):475-86.
46. Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, Owen RM, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov. 2015;14 (7):475-86.
47. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18 (1):41-58.
48. Breckenridge A, Jacob R. Overcoming the legal and regulatory barriers to drug repurposing. Nat Rev Drug Discov. 2019;18 (1):1-2.
49. Nosengo N. Can you teach old drugs new tricks? Nature. 2016;534 (7607):314-6.
50. Pfizer’s Expiring Viagra Patent Adversely Affects Other Drugmakers Too. Forbes. 2013 December 20, 2013.
51. Urquhart L. Market watch: Top drugs and companies by sales in 2017. Nat Rev Drug Discov. 2018 Mar 28;17(4):232.
52. Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013 Apr;93(4):335-41.
53. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462 (7270):175-81.
54. Hieronymus H, Lamb J, Ross KN, Peng XP, Clement C, Rodina A, et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell. 2006;10 (4):321-30.
55. Dudley JT, Deshpande T, Butte AJ. Exploiting drugdisease relationships for computational drug repositioning. Brief Bioinform. 2011 Jul;12(4):303-11.
56. Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning? Drug Discov Today. 2013 Apr;18(7-8):350-7.
57. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011 Aug 17;3(96):96ra76.
58. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011 Aug 17;3(96):96ra77.
59. Wei G, Twomey D, Lamb J, Schlis K, Agarwal J, Stam RW, et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell. 2006 Oct;10(4):331- 42.
60. Chiang AP, Butte AJ. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther. 2009 Nov;86(5):507-10.
61. Oprea TI, Tropsha A, Faulon JL, Rintoul MD. Systems chemical biology. Nat Chem Biol. 2007 Aug;3(8):447-50.
62. Emig D, Ivliev A, Pustovalova O, Lancashire L, Bureeva S, Nikolsky Y, et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS One. 2013;8 (4):e60618.
63. Wu Z, Wang Y, Chen L. Network-based drug repositioning. Mol Biosyst. 2013 Jun;9(6):1268-81.
64. Weeber M, Klein H, De Jong-Van Den Berg L, Vos R. Using concepts in literature-based discovery: Simulating Swanson’s Raynaud–fish oil and migraine–magnesium discoveries. JASIST. 2001;52 (7):548-57.
65. Xue H, Li J, Xie H, Wang Y. Review of Drug Repositioning Approaches and Resources. Int J Biol Sci. 2018 Jul 13;14(10):1232-1244.
66. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014 Jan;32(1):40-51.
67. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20 (2):273-86.
68. CLINPAL. Recruitment infographic 2021 [Available from: https://www.clinpal.com/recruitment-infographic/.
69. Harrer S, editor Measuring life: sensors and analytics for precision medicine. SPIE Microtechnologies; 2015; Barcelona: Proc. SPIE 9518, Bio-MEMS and Medical Microdevices II, 951802.
70. Banda JM, Seneviratne M, Hernandez-Boussard T, Shah NH. Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models. Annu Rev Biomed Data Sci. 2018;1:53-68.
71. Goudey B, Fung BJ, Schieber C, Faux NG; Alzheimer’s Disease Metabolomics Consortium; Alzheimer’s Disease Neuroimaging Initiative. A blood-based signature of cerebrospinal fluid Aβ1-42 status. Sci Rep. 2019 Mar 11;9(1):4163. doi: 10.1038/s41598-018-37149-7.
72. Palmqvist S, Insel PS, Zetterberg H, Blennow K, Brix B, Stomrud E, et al. Accurate risk estimation of betaamyloid positivity to identify prodromal Alzheimer’s disease: Cross-validation study of practical algorithms. Alzheimers Dement. 2019 Feb;15(2):194-204.
73. Ghosh S, Sun Z, Li Y, Cheng Y, Mohan A, Sampaio C, et al. An Exploration of Latent Structure in Observational Huntington’s Disease Studies. AMIA Jt Summits Transl Sci Proc. 2017;2017:92-102.
74. Sun Z, Li Y, Ghosh S, Cheng Y, Mohan A, Sampaio C, et al. A Data-Driven Method for Generating Robust Symptom Onset Indicators in Huntington’s Disease Registry Data. AMIA Annu Symp Proc. 2017;2017:1635-44.
75. Sun Z, Ghosh S, Li Y, Cheng Y, Mohan A, Sampaio C, et al. A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data. JAMA Open. 2019;2 (1):123- 30.
76. Schobel SA, Palermo G, Auinger P, Long JD, Ma S, Khwaja OS, et al. Motor, cognitive, and functional declines contribute to a single progressive factor in early HD. Neurology. 2017 Dec 12;89(24):2495-2502.
77. Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp Clin Trials Commun. 2018 Aug 7;11:156-164.
78. LeCun Y. The Power and Limits of Deep Learning: In his IRI Medal address, Yann LeCun maps the development of machine learning techniques and suggests what the future may hold. Res Technol Manag. 2018;61 (6):22-7.
79. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44.

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[1]
Matsingos, C. et al. 2022. Aplicaciones de la inteligencia artificial en la farmacología básica y clínica. Medicina. 43, 4 (ene. 2022), 652–667. DOI:https://doi.org/10.56050/01205498.1652.

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2022-01-18

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