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Review

Harnessing machine learning for development of microbiome therapeutics

ORCID Icon, ORCID Icon, , &
Article: 1872323 | Received 27 Nov 2020, Accepted 20 Dec 2020, Published online: 30 Jan 2021

References

  • Rosa BA, Mihindukulasuriya K, Hallsworth-Pepin K, Wollam A, Martin J, Snowden C, et al. Improving characterization of understudied human microbiomes using targeted phylogenetics [Article]. mSystems. 2020;5:1. doi:10.1128/mSystems.00096-20.
  • Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010; 464:59-65. doi: 10.1038/nature08821.
  • Singer-Englar T, Barlow G, Mathur R. Obesity, diabetes, and the gut microbiome: an updated review. Expert Rev Gastroenterol Hepatol. 2019 Jan;13(1):3–20. doi:10.1080/17474124.2019.1543023.
  • Dutta SK, Verma S, Jain V, Surapaneni BK, Vinayek R, Phillips L, et al.  Parkinson’s disease: the emerging role of gut dysbiosis, antibiotics, probiotics, and fecal microbiota transplantation. J Neurogastroenterol Motil. 2019 Jul 1;530(1–2):363–376. doi:10.5056/jnm19044.
  • Yadav V, Varum F, Bravo R, Furrer E, Bojic D, Basit AW. Inflammatory bowel disease: exploring gut pathophysiology for novel therapeutic targets. Transl Res. 2016 Oct;176:38–68. doi:10.1016/j.trsl.2016.04.009.
  • Gao L, Xu T, Huang G,Jiang S, Gu Y, Chen F. Oral microbiomes: more and more importance in oral cavity and whole body. Protein Cell. 2018 May;9(5):488–500. doi:10.1007/s13238-018-0548-1.
  • Ercolini D, Fogliano V. Food design to feed the human gut microbiota. J Agric Food Chem. 2018;66(15):3754–3758. doi:10.1021/acs.jafc.8b00456.
  • Ezra-Nevo G, Henriques SF, Ribeiro C. The diet-microbiome tango: how nutrients lead the gut brain axis. Curr Opin Neurobiol. 2020 [2020/06/01/];62:122–132. doi:10.1016/j.conb.2020.02.005.
  • Valdes AM, Walter J, Segal E,Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018;361:k2179. doi:10.1136/bmj.k2179.
  • Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R,Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570(7762):462–467. doi:10.1038/s41586-019-1291-3.
  • Clarke G, Sandhu KV, Griffin BT,Dinan TG, Cryan JF, Hyland NP. Gut reactions: breaking down xenobiotic-microbiome interactions. Pharmacol Rev. 2019 Apr;71(2):198–224. doi:10.1124/pr.118.015768.
  • Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012 2012/06/01;486(7402):207–214.doi: 10.1038/nature11234.
  • Proctor LM, Creasy HH, Fettweis JM, Lloyd-Price J, Mahurkar A, Zhou W, et al. The integrative human microbiome project. Nature. 2019 2019/05/01;569(7758):641–648.doi: 10.1038/s41586-019-1238-8.
  • Human Microbiome HMPNIH. Project. Maryland, USA: National Institutes of Health (NIH); 2020.
  • EuPathDB. MicrobiomeDB. USA: National. Institute of Allergy and Infectious Diseases (NIAD); 2020.
  • China National GeneBank. Microbiome Database. Shenzhen (China): China National GeneBank; 2020.
  • Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–98. doi:10.7861/futurehosp.6-2-94. doi: 10.7861/futurehosp.6-2-94.
  • Camacho DM, Collins KM, Powers RK,Costello JC, Collins JJ. Next-generation machine learning for biological networks [review]. Cell. 2018;173(7):1581–1592. doi:10.1016/j.cell.2018.05.015
  • Namkung J. Machine learning methods for microbiome studies. J Microbiol. 2020 Mar;58(3):206–216. doi:10.1007/s12275-020-0066-8.
  • Vujkovic-Cvijin I, Sklar J, Jiang L,Natarajan L, Knight R, Belkaid Y. Host variables confound gut microbiota studies of human disease. Nature. 2020 Nov;587(7834):448–454. doi:10.1038/s41586-020-2881-9.
  • Google. A history of machine learning 2020 [Accessed 20th October 2020]. Available from: https://cloud.withgoogle.com/build/data-analytics/explore-history-machine-learning/
  • Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam‐Thong T, Siebourg‐Polster J, Steiert B, Zhang JD. An introduction to machine learning. . Clinical Pharmacology & Therapeutics. 2020 Apr;;107(4):871–885. doi:10.1002/cpt.1796.
  • Neftci EO, Averbeck BB. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence. 2019/03/01 2019;1(3):133–143. doi:10.1038/s42256-019-0025-4.
  • Sledge IJ, Príncipe JC, editors. Balancing exploration and exploitation in reinforcement learning using a value of information criterion. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2017 5–9 March 2017. doi:10.1109/ICASSP.2017.7952670.
  • Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature. 2017 2017/10/01;550(7676):354–359. doi: 10.1038/nature24270
  • Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, et al. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nature reviews gastroenterology & hepatology. 2020; 17:635-648.
  • ChenM, MaoS, Liu, Y. Big Data:A Survey. Mobile networks and applications. 2014;19(2):171–209. https://doi.org/10.1007/s11036-013-0489-0.
  • Tang Q, Jin G, Wang G, Liu T, Liu X, Wang B, et al. Current sampling methods for gut microbiota: a call for more precise devices. Front Cell Infect Microbiol. 2020;10:151. doi:10.3389/fcimb.2020.00151.
  • Vuik F, Dicksved J, Lam SY, Fuhler GM, van der Laan L, van de Winkel A, et al. Composition of the mucosa-associated microbiota along the entire gastrointestinal tract of human individuals. United European Gastroenterol J. 2019 Aug;7(7):897–907. doi:10.1177/2050640619852255.
  • James KR, Gomes T, Elmentaite R, Kumar N, Gulliver EL, King HW, et al. Distinct microbial and immune niches of the human colon. Nat Immunol. 2020 Mar;21(3):343–353. doi: 10.1038/s41590-020-0602-z.
  • Parmanand BA, Kellingray L, Le Gall G, Basit AW, Fairweather-Tait S, Narbad A. A decrease in iron availability to human gut microbiome reduces the growth of potentially pathogenic gut bacteria; an in vitro colonic fermentation study. J Nutr Biochem. 2019 May;67:20–27. doi:10.1016/j.jnutbio.2019.01.010.
  • Wang J, Yadav V, Smart AL, Tajiri S, Basit AW. Stability of peptide drugs in the colon. Eur J Pharm Sci. 2015 Oct;78(78):31–36. doi:10.1016/j.ejps.2015.06.018.
  • Coombes Z, Yadav V, E. McCoubrey L, Freire C, W. Basit A, Conlan RS, et al. Progestogens are metabolized by the gut microbiota: implications for colonic drug delivery. Pharmaceutics. 2020;12(8). doi:10.3390/pharmaceutics12080760.
  • Chaudhari DS, Dhotre DP, Agarwal DM, Gaike AH, Bhalerao D, Jadhav P, et al. Gut, oral and skin microbiome of indian patrilineal families reveal perceptible association with age. Sci Rep. 2020 ;2020/03/30 10(1):5685. doi: 10.1038/s41598-020-62195-5
  • Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature. 2018 2018/03/01;555(7698):623–628. doi: 10.1038/nature25979
  • Freire AC, Basit AW, Choudhary R, Piong CW, Merchant HA. Does sex matter? the influence of gender on gastrointestinal physiology and drug delivery. Int J Pharm. 2011 Aug 30;415(1–2):15–28. doi:10.1016/j.ijpharm.2011.04.069.
  • Dehingia M, Adak A, Khan MR. Ethnicity-influenced microbiota: a future healthcare perspective. Trends Microbiol. 2019/03/01/ 2019;27(3):191–193. doi:10.1016/j.tim.2019.01.002.
  • So D, Whelan K, Rossi M, Morrison M, Holtmann G, Kelly J, et al.  Dietary fiber intervention on gut microbiota composition in healthy adults: A systematic review and meta-analysis. Am J Clin Nutr. 2018;05/11;107.doi: 10.1093/ajcn/nqy041.
  • Vujkovic-Cvijin I, Sklar J, Jiang L, Natarajan L, Knight R, Belkaid Y.  Host variables confound gut microbiota studies of human disease. In: Nature. 2020; 587(7834):448-454. doi: 10.1038/s41586-020-2881-9.
  • Raudys SJ, Jain AK. Small sample size effects in statistical pattern recognition: recommendations for practitioners [article]. IEEE Trans Pattern Anal Mach Intell. 1991;13(3):252–264. doi:10.1109/34.75512.
  • Lecun Y, Bengio Y, Hinton G. Deep learning [Review]. Nature. 2015;521(7553):436–444. doi:10.1038/nature14539.
  • Vabalas A, Gowen E, Poliakoff E, Casson AJ.  Machine learning algorithm validation with a limited sample size. PLoS One. 2019;12(3):e0224365. doi:10.1371/journal.pone.0224365.
  • Cheng M, Cao L, Microbiome Big-Data NK. Mining and applications using single-cell technologies and metagenomics approaches toward precision medicine [review]. Front Genet. 2019; 10: 972. 2019-October-09. doi: 10.3389/fgene.2019.00972.
  • Cox DR, Kartsonaki C, Keogh RH. Big data: some statistical issues. Stat Probab Lett. 2018 [2018/05/01/];136:111–115. doi:10.1016/j.spl.2018.02.015.
  • García-Gil D, Luengo J, García S, Herrera F. Enabling smart data: noise filtering in big data classification. Inf Sci (Ny). 2019 . 479:135–152. 2019/04/01/.
  • Lazer D, Kennedy R, King G, Vespignani A.  The parable of google flu: traps in big data analysis. Science. 2014;343(6176):1203–1205. doi:10.1126/science.1248506.
  • Anonymous. Not so big. The Economist 2020. Jun 2020;13:S5–S6.
  • Géron A. Hands-on machine learning with scikit-learn & tensorFlow. First ed. California, USA: O’Reilly; 2017.
  • Morris TP, White IR, Royston P. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol. 2014/06/05 2014;14(1):75. doi:10.1186/1471-2288-14-75.
  • Koprinkova P, Petrova M. Data-scaling problems in neural-network training [Article]. Eng Appl Artif Intell. 1999;12(3):281–296. doi:10.1016/S0952-1976(99)00008-1.
  • Sharma AK, Jaiswal SK, Chaudhary N, Sharma VK. A novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota. Sci Rep. 2017 2017 Aug 29;7(1):9751. doi:10.1038/s41598-017-10203-6.
  • Noronha A, Modamio J, Jarosz Y, Guerard E, Sompairac N, Preciat G, et al. The virtual metabolic human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 2019 Jan 8;47(D1):D614–D624. doi:10.1093/nar/gky992.
  • Han W, Ye Y. A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2019;24:236–247. PMID: 30864326.
  • Nguyen HT, Phan NYK, Luong HH, Le TP, Tran NC. Efficient discretization approaches for machine learning techniques to improve disease classification on gut microbiome composition data [Article]. Advances in Science, Technology and Engineering Systems. 2020;5(3):547–556. doi:10.25046/aj050368.
  • Chandrasekaran SN, Ceulemans H, Boyd JD, Carpenter AE. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat rev drug discov. 2020 Dec 22.doi: 10.1038/s41573-020-00117-w.
  • Zhou YH, Gallins P. A review and tutorial of machine learning methods for microbiome host trait prediction [Article]. Front Genet. 2019;10(JUN). doi:10.3389/fgene.2019.00579.
  • Ghyselinck J, Verstrepen L, Moens F, Van den Abbeele P, Said J, Smith B, et al. A 4-strain probiotic supplement influences gut microbiota composition and gut wall function in patients with ulcerative colitis. Int J Pharm. 2020 Jul;587:119648. doi:10.1016/j.ijpharm.2020.119648.
  • Barlow GM, Yu A, Mathur R. Role of the gut microbiome in obesity and diabetes mellitus. Nutr Clin Pract. 2015 Dec;30(6):787–797. doi:10.1177/0884533615609896.
  • Rowin J, Xia Y, Jung B, Sun J.Gut inflammation and dysbiosis in human motor neuron disease [Article]. Physiological Reports. 2017;5(18):18. doi:10.14814/phy2.13443.
  • Moens F, Van den Abbeele P, Basit AW, Dodoo C, Chatterjee R, Smith B, et al.  A four-strain probiotic exerts positive immunomodulatory effects by enhancing colonic butyrate production in vitro. Int J Pharm. 2019. 555:1–10. 2019/01/30/. doi: 10.1016/j.ijpharm.2018.11.020
  • Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights [article]. PLoS Comput Biol. 2016;12(7):7. doi:10.1371/journal.pcbi.1004977.
  • Gupta VK, Kim M, Bakshi U, Cunningham KY, Davis JM, Lazaridis KN, et al. A predictive index for health status using species-level gut microbiome profiling. Nat Commun. 2020;11(1):1. doi:10.1038/s41467-020-18476-8.
  • Lian X, Yang S, Li H, Fu C, Zhang Z. Machine-learning-based predictor of human-bacteria protein-protein interactions by incorporating comprehensive host-network properties [article]. J Proteome Res. 2019;18(5):2195–2205. doi:10.1021/acs.jproteome.9b00074.
  • Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015 Nov 19;163(5):1079–1094. doi:10.1016/j.cell.2015.11.001.
  • Veiga P, Suez J, Derrien M, Elinav E. Moving from probiotics to precision probiotics. Nat Microbiol. 2020 May 11; 5: 878-880. doi: 10.1038/s41564-020-0721-1.
  • Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery [Review]. Drug Discov Today. 2017;22(11):1680–1685. doi:10.1016/j.drudis.2017.08.010.
  • Stephenson N, Shane E, Chase J, Rowland J, Ries D, Justice N, et al. Survey of machine learning techniques in drug discovery [review]. Curr Drug Metab. 2019;20(3):185–193. doi:10.2174/1389200219666180820112457.
  • Elbadawi M, Gaisford S, Basit AW. Advanced machine-learning techniques in drug discovery. Drug discov today. 2020 Dec 5. doi: 10.1016/j.drudis.2020.12.003
  • Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2017;2017/09/04 9(1):48. doi: 10.1186/s13321-017-0235-x
  • Chen H, Zhang Z.A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks PLoS One. 2013;8:5. doi: 10.1371/journal.pone.0062975.
  • Bleakley K, Yamanishi Y. Supervised prediction of drug-target interactions using bipartite local models [Article]. Bioinformatics. 2009;25(18):2397–2403. doi:10.1093/bioinformatics/btp433.
  • Taguchi YH, Iwadate M, Umeyama H. Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease [Article]. BMC Bioinform. 2015;16(1):1. doi:10.1186/s12859-015-0574-4.
  • Zhou H, Gao M, Skolnick J. Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci Rep. 2015/06/09 2015;5(1):11090. doi:10.1038/srep11090.
  • Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, Fu H, Xue X, Lu C, Ma J, et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science. 2018;359(6380):1151. doi:10.1126/science.aao5774.
  • Byrne CS, Chambers ES, Preston T, Tedford C, Brignardello J, Garcia-Perez I, Holmes E, Wallis GA, Morrison DJ, Frost GS, et al. Effects of Inulin propionate ester incorporated into palatable food products on appetite and resting energy expenditure: A randomised crossover study [Article]. Nutrients. 2019;11(4):4. doi:10.3390/nu11040861.
  • Koziolek M, Alcaro S, Augustijns P, Basit AW, Grimm M, Hens B, Hoad CL, Jedamzik P, Madla CM, Maliepaard M, et al. The mechanisms of pharmacokinetic food-drug interactions – A perspective from the UNGAP group. European Journal of Pharmaceutical Sciences. 2019. 134:31–59. 2019/06/15/. doi: 10.1016/j.ejps.2019.04.003
  • Lam K-L, Cheng W-Y, Su Y, Li X, Wu X, Wong K-H, et al. Use of random forest analysis to quantify the importance of the structural characteristics of beta-glucans for prebiotic development. Food Hydrocolloids. 2020;108.doi: 10.1016/j.foodhyd.2020.106001.
  • Dodoo CC, Stapleton P, Basit AW, Gaisford S. Use of a water-based probiotic to treat common gut pathogens. Int J Pharm. 2019 Feb;556(556):136–141. doi:10.1016/j.ijpharm.2018.11.075.
  • Earley H, Lennon G, Balfe A, Coffey JC, Winter DC, O'Connell PR. The abundance of Akkermansia muciniphila and its relationship with sulphated colonic mucins in health and ulcerative colitis. Sci Rep. 2019 Oct 30;9(1):15683. doi:10.1038/s41598-019-51878-3.
  • Fadda HM. The route to palatable fecal microbiota transplantation. AAPS PharmSciTech. 2020;21(3):3. doi:10.1208/s12249-020-1637-z.
  • Allegretti J, Eysenbach LM, El-Nachef N, Fischer M, Kelly C, Kassam Z. The current landscape and lessons from fecal microbiota transplantation for inflammatory bowel disease: past, present, and future [review]. Inflamm Bowel Dis. 2017;23(10):1710–1717. doi:10.1097/MIB.0000000000001247.
  • Allegretti JR, Fischer M, Sagi SV, Bohm ME, Fadda HM, Ranmal SR, et al. Fecal microbiota transplantation capsules with targeted colonic versus gastric delivery in recurrent clostridium difficile infection: a comparative cohort analysis of high and lose dose. Dig Dis Sci. 2019 Jun;64(6):1672–1678. doi:10.1007/s10620-018-5396-6.
  • Deusch S, Serrano-Villar S, Rojo D, Martínez-Martínez M, Bargiela R, Vázquez-Castellanos JF, et al. Effects of HIV, antiretroviral therapy and prebiotics on the active fraction of the gut microbiota [Article]. AIDS. 2018;32(10):1229–1237. doi:10.1097/QAD.0000000000001831.
  • Serrano-Villar S, Vázquez-Castellanos JF, Vallejo A, Latorre A, Sainz T, Ferrando-Martínez S, et al. The effects of prebiotics on microbial dysbiosis, butyrate production and immunity in HIV-infected subjects. Mucosal Immunol. 2017 2017/09/01;10(5):1279–1293. doi: 10.1038/mi.2016.122
  • Daisley BA, Chanyi RM, Abdur-Rashid K, Al KF, Gibbons S, Chmiel JA, et al.  Abiraterone acetate preferentially enriches for the gut commensal Akkermansia muciniphila in castrate-resistant prostate cancer patients. Nat Commun. 2020 Sep 24;11(1):4822. doi:10.1038/s41467-020-18649-5.
  • Chambers ES, Byrne CS, Rugyendo A, Morrison DJ, Preston T, Tedford C, et al. The effects of dietary supplementation with inulin and inulin-propionate ester on hepatic steatosis in adults with non-alcoholic fatty liver disease. Diabetes Obes Metab. 2019;21(2):372–376. doi:10.1111/dom.13500.
  • Chambers ES, Viardot A, Psichas A, Morrison DJ, Murphy KG, Zac-Varghese SEK, et al. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut. 2015;64(11):1744–1754. doi:10.1136/gutjnl-2014-307913.
  • Langdon A, Crook N, Dantas G. The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation. Genome Med. 2016;8(1):39. doi:10.1186/s13073-016-0294-z.
  • Ghose C, Euler CW. Gram-negative bacterial lysins [Review]. Antibiotics. 2020;9(2):2. doi:10.3390/antibiotics9020074.
  • Aulton ME, Taylor K. Aulton’s pharmaceutics: the design and manufacture of medicines. Edinburgh, United Kingdom: Churchill Livingstone/Elsevier; 2018. English.
  • McConnell EL, Liu F, Basit AW. Colonic treatments and targets: issues and opportunities. J Drug Target. 2009 Jun;17(5):335–363. doi:10.1080/10611860902839502.
  • Bak A, Ashford M, Brayden DJ. Local delivery of macromolecules to treat diseases associated with the colon. Adv Drug Deliv Rev. 2018 Nov;136-137:2–27. Dec;136-137. doi:10.1016/j.addr.2018.10.009.
  • Varum F, Cristina Freire A, Bravo R, Basit AW. OPTICORE, an innovative and accurate colonic targeting technology. Int J Pharm. 2020 Apr;583:119372. doi:10.1016/j.ijpharm.2020.119372.
  • Varum F, Cristina Freire A, Fadda HM, Bravo R, Basit AW.  A dual pH and microbiota-triggered coating (Phloral(TM)) for fail-safe colonic drug release. Int J Pharm. 2020 Apr;583:119379. doi:10.1016/j.ijpharm.2020.119379.
  • Damiati SA. digital pharmaceutical sciences. AAPS PharmSciTech. 2020 Jul 26;21(6):206. doi:10.1208/s12249-020-01747-4.
  • Yang Y, Ye Z, Su Y, Zhao Q, Li X, Ouyang D. Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm Sin B. 2019;9(1):177–185. 2019/01/01/. doi:10.1016/j.apsb.2018.09.010.
  • Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules [Article]. J Chem Inf Model. 2013;53(7):1563–1575. doi:10.1021/ci400187y.
  • Han R, Yang Y, Li X, Ouyang D. Predicting oral disintegrating tablet formulations by neural network techniques. Asian J Pharm Sci. 2018;13(4):336–342. Jul;. doi:10.1016/j.ajps.2018.01.003.
  • Han R, Xiong H, Ye Z, Yang Y, Huang T, Jing Q, et al.  Predicting physical stability of solid dispersions by machine learning techniques. Journal of Controlled Release. 2019;2019/10/01/;311–312:16–25.doi: 10.1016/j.jconrel.2019.08.030.
  • Gentiluomo L, Roessner D, Augustijn D, Svilenov H, Kulakova A, Mahapatra S, et al. Application of interpretable artificial neural networks to early monoclonal antibodies development. European Journal of Pharmaceutics and Biopharmaceutics. 2019;2019/08/01/;141:81–89. https://doi.org/10.1016/j.ejpb.2019.05.017.
  • Li Y, Abbaspour MR, Grootendorst PV, Rauth AM, Wu XY. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology. European Journal of Pharmaceutics and Biopharmaceutics. 2015;2015/08/01/;94:170–179.https://doi.org/10.1016/j.ejpb.2015.04.028.
  • Barthus RC, Mazo LH, Poppi RJ. Simultaneous determination of vitamins C, B6 and PP in pharmaceutics using differential pulse voltammetry with a glassy carbon electrode and multivariate calibration tools. J Pharm Biomed Anal. 2005 2005/06//;38(1):94–99. doi:10.1016/j.jpba.2004.12.017.
  • Ghosh A, Louis L, Arora KK, Hancock BC, Krzyzaniak JF, Meenan P, et al. Assessment of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredients [Article]. CrystEngComm. 2019;21(8):1215–1223.doi: 10.1039/C8CE01589A.
  • Dodoo CC, Wang J, Basit AW, Stapleton P, Gaisford S. Targeted delivery of probiotics to enhance gastrointestinal stability and intestinal colonisation. Int J Pharm. 2017;530(1):224–229. 2017/09/15/. doi:10.1016/j.ijpharm.2017.07.068.
  • King AC, Woods M, Liu W, Lu Z, Gill D, Krebs MRH. High-throughput measurement, correlation analysis, and machine-learning predictions for pH and thermal stabilities of Pfizer-generated antibodies [Article]. Protein Science. 2011;20(9):1546–1557. doi:10.1002/pro.680.
  • Singhal A, Roy D, Mittal S, Dhar J, Singh A. A new computational approach to identify essential genes in bacterial organisms using machine learning. Advances in Intelligent Systems and Computing. 2019(p):67–79.doi: 10.1007/978-981-13-1132-1_6.
  • Lee MW, de Anda J, Kroll C, Bieniossek C, Bradley K, Amrein KE, et al. How do cyclic antibiotics with activity against Gram-negative bacteria permeate membranes? A machine learning informed experimental study [Article]. Biochimica Et Biophysica Acta - Biomembranes. 2020;1862:8. doi:10.1016/j.bbamem.2020.183302.
  • Khaledi A, Weimann A, Schniederjans M, Asgari E, Kuo TH, Oliver A, et al. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics. EMBO Mol Med. 2020;12(3):3. doi:10.15252/emmm.201910264.
  • Luo YM, Liu FT, Chen MX, Tang WL, Yang YL, Tan XL, et al. A machine learning model based on initial gut microbiome data for predicting changes of bifidobacterium after prebiotics consumption [article]. Nan Fang Yi Ke Da Xue Xue Bao (Journal of Southern Medical University). 2018;38(3):251–260. doi: 10.3969/j.issn.1673-4254.2018.03.03.
  • Goyanes A, Madla CM, Umerji A, Duran Piñeiro G, Giraldez Montero JM, Lamas Diaz MJ, et al. Automated therapy preparation of isoleucine formulations using 3D printing for the treatment of MSUD: first single-centre, prospective, crossover study in patients [Article]. Int J Pharm. 2019;567. doi: 10.1016/j.ijpharm.2019.118497.
  • Trenfield SJ, Awad A, Madla CM, Hatton GB, Firth J, Goyanes A, et al.  Shaping the future: recent advances of 3D printing in drug delivery and healthcare [Review]. Expert Opin Drug Deliv. 2019;16(10):1081–1094. doi:10.1080/17425247.2019.1660318.
  • Awad A, Yao A, Trenfield SJ, Goyanes A, Gaisford S, Basit AW.  3D printed tablets (Printlets) with braille and moon patterns for visually impaired patients [Article]. Pharmaceutics. 2020;12(2):2. doi:10.3390/pharmaceutics12020172.
  • Elbadawi M, Ong JJ, Pollard TD, Gaisford S, Basit AW. Additive manufacturable materials for electrochemical biosensor electrodes. Adv Funct Mater. 2020. doi:10.1002/adfm.202006407.
  • Norman J, Madurawe RD, Moore CM, Khan MA, Khairuzzaman A. A new chapter in pharmaceutical manufacturing: 3D-printed drug products. Adv Drug Deliv Rev 2017 Jan 1;108:39–50. doi: 10.1016/j.addr.2016.03.001
  • Melocchi A, Briatico-Vangosa F, Uboldi M, Parietti F, Turchi M, von Zeppelin D, et al. Quality considerations on the pharmaceutical applications of fused deposition modeling 3D printing. Int J Pharm. 2021;592:119901. 2021 Jan 5. doi:10.1016/j.ijpharm.2020.119901.
  • Lim SH, Kathuria H, Tan JJY, Kang L. 3D printed drug delivery and testing systems - a passing fad or the future? Advanced Drug Delivery Reviews. 2018;132:139–168. 2018 Jul. doi:10.1016/j.addr.2018.05.006.
  • Gioumouxouzis CI, Karavasili C, Fatouros DG. Recent advances in pharmaceutical dosage forms and devices using additive manufacturing technologies. Drug Discov Today. 2019 Feb;24(2):636–643. doi:10.1016/j.drudis.2018.11.019.
  • Alhnan MA, Okwuosa TC, Sadia M, Wan KW, Ahmed W, Arafat B. Emergence of 3D printed dosage forms: opportunities and challenges. Pharm Res. 2016 Aug;33(8):1817–1832. doi: 10.1007/s11095-016-1933-1.
  • Chen G, Xu Y, Chi Lip Kwok P, Kang L. Pharmaceutical applications of 3d printing. Additive Manufacturing. 2020;2020/08/01/;34:101209. https://doi.org/10.1016/j.addma.2020.101209.
  • Awad A, Fina F, Goyanes A, Gaisford S, Basit AW. 3D printing: principles and pharmaceutical applications of selective laser sintering. Int J Pharm. 2020;586:119594. 2020 Aug 30. doi:10.1016/j.ijpharm.2020.119594.
  • Franzosa EA, Huang K, Meadow JF, Gevers D, Lemon KP, Bohannan BJM, et al. Identifying personal microbiomes using metagenomic codes. Proceedings of the National Academy of Sciences. 2015;201423854.doi: 10.1073/pnas.1423854112.
  • Merchant HA, Liu F, Orlu Gul M, Basit AW.  Age-mediated changes in the gastrointestinal tract. Int J Pharm. 2016;512(2):382–395. 2016/10/30/. doi:10.1016/j.ijpharm.2016.04.024.
  • Hatton GB, Madla CM, Rabbie SC, Basit AW. All disease begins in the gut: influence of gastrointestinal disorders and surgery on oral drug performance. Int J Pharm. 2018 2018 Sep 5;548(1):408–422. doi:10.1016/j.ijpharm.2018.06.054.
  • Hatton GB, Madla CM, Rabbie SC, Basit AW. Gut reaction: impact of systemic diseases on gastrointestinal physiology and drug absorption. Drug Discov Today. 2019;24(2):417–427. 2019/02/01/. doi:10.1016/j.drudis.2018.11.009.
  • Capel AJ, Rimington RP, Lewis MP, Christie SDR. 3D printing for chemical, pharmaceutical and biological applications. Nature Reviews Chemistry. 2018 2018/12/01;2(12):422–436. doi:10.1038/s41570-018-0058-y.
  • Xu X, Awad A, Robles-Martinez P, Gaisford S, Goyanes A, Basit AW. Vat photopolymerization 3D printing for advanced drug delivery and medical device applications. J Control Release 2020 Oct 5.doi: 10.1016/j.jconrel.2020.10.008.
  • Durga Prasad Reddy R, Sharma V. Additive manufacturing in drug delivery applications: A Review. Int J Pharm. Sep 2020;4:119820. doi: 10.1016/j.ijpharm.2020.119820.
  • Dodoo CC, Stapleton P, Basit AW, Gaisford S.  The potential of streptococcus salivarius oral films in the management of dental caries: an inkjet printing approach [Article]. Int J Pharm. 2020;591:591. doi:10.1016/j.ijpharm.2020.119962.
  • Elbadawi M, Gustaffson T, Gaisford S, Basit AW. 3D printing tablets: predicting printability and drug dissolution from rheological data. Int J Pharm. 2020;590:119868. 2020/11/30/. doi:10.1016/j.ijpharm.2020.119868.
  • Trenfield SJ, Tan HX, Goyanes A, Wilsdon D, Rowland M, Gaisford S, Basit AW. Non-destructive dose verification of two drugs within 3D printed polyprintlets. Int J Pharm. 2020;577:577. doi:10.1016/j.ijpharm.2020.119066.
  • Elbadawi M, Muniz Castro B, Gavins FKH, Ong JJ, Gaisford S, Perez G, et al. M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines. Int J Pharm 2020 Sep 20;590:119837. doi: 10.1016/j.ijpharm.2020.119837
  • Perez GIP, Gao Z, Jourdain R, Ramirez J, Gany F, Clavaud C, et al. Body site is a more determinant factor than human population diversity in the healthy skin microbiome [Article]. PLoS One. 2016;11(4):4. doi:10.1371/journal.pone.0151990.
  • Scheline RR. Metabolism of foreign compounds by gastrointestinal microorganisms. Pharmacol Rev. 1973;25:451. https://doi.org/10.1002/jps.2600571202.
  • Sousa T, Paterson R, Moore V, Carlsson A, Abrahamsson B, Basit AW. The gastrointestinal microbiota as a site for the biotransformation of drugs. Int J Pharm. 2008 2008 Nov 03;363(1–2):1–25. doi:10.1016/j.ijpharm.2008.07.009.
  • Crouwel F, Buiter HJC, de Boer NK. Gut microbiota-driven drug metabolism in inflammatory bowel disease. J Crohns Colitis. 2020 Jul. 11. doi: 10.1093/ecco-jcc/jjaa143
  • Hitchings R, Kelly KL. Predicting and understanding the human microbiome’s impact on pharmacology. Trends in Pharmacological Sciences. 2019 Jul;40(7):495–505. doi:10.1016/j.tips.2019.04.014.
  • Koppel N, Bisanz JE, Pandelia M-E, Turnbaugh PJ, Balskus EP. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. eLife. 2018;7:7. doi:10.7554/eLife.33953.
  • van Kessel SP, Frye AK, El-Gendy AO, Castejon M, Keshavarzian A, van Dijk G, El Aidy S. Gut bacterial tyrosine decarboxylases restrict levels of levodopa in the treatment of parkinson’s disease. Nat Commun. 2019;10(1):310. doi:10.1038/s41467-019-08294-y.
  • Borrel G, Brugere J-F, Gribaldo S, Schmitz RA, Moissl-Eichinger C. The host-associated archaeome. Nature Reviews. Microbiology. 2020 2020 Jul;18(11):20. doi:10.1038/s41579-020-0407-y.
  • Sokol H, Leducq V, Aschard H, Pham H-P, Jegou S, Landman C, Cohen D, Liguori G, Bourrier A, Nion-Larmurier I, et al. Fungal microbiota dysbiosis in IBD. Gut. 2017;66(6):1039. doi:10.1136/gutjnl-2015-310746.
  • Dong Q, Brulc JM, Iovieno A, Bates B, Garoutte A, Miller D, Revanna KV, Gao X, Antonopoulos DA, Slepak VZ, et al. Diversity of bacteria at healthy human conjunctiva [Article]. Investigative Opthalmology & Visual Science. 2011;52(8):5408–5413. doi:10.1167/iovs.10-6939.
  • Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. Bacterial community variation in human body habitats across space and time. Science. 2009;326(5960):1694. doi:10.1126/science.1177486.
  • Pollard TD, Ong JJ, Goyanes A, Orlu M, Gaisford S, Elbadawi M, Basit AW. Electrochemical biosensors: a nexus for precision medicine. Drug discovery Today. 2020; 31. 2020 Oct. doi:10.1016/j.drudis.2020.10.021.
  • Palmara G, Frascella F, Roppolo I, Chiappone A, Chiadò A. Functional 3D printing: approaches and bioapplications. Biosens Bioelectron 2020 Nov 24:112849.doi: 10.1016/j.bios.2020.112849.
  • Beri K. Skin microbiome & host immunity: applications in regenerative cosmetics & transdermal drug delivery. Future Science OA. 2018;4(6):302. doi:10.4155/fsoa-2017-0117.
  • Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB, Klein TE. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012 2012/10/01;92(4):414–417. doi:10.1038/clpt.2012.96.
  • Nava Lara RA, Aguilera-Mendoza L, Brizuela CA, Peña A, Del Rio G. Heterologous machine learning for the identification of antimicrobial activity in human-targeted drugs. Molecules (Basel, Switzerland). 2019 2019 Mar 31;24(7):7. doi:10.3390/molecules24071258.
  • Zheng S, Chang W, Liu W, Liang G, Xu Y, Lin F. Computational prediction of a new admet endpoint for small molecules: anticommensal effect on human gut microbiota. J Chem Inf Model. 2019 2019 Mar 25;59(3):1215–1220. doi:10.1021/acs.jcim.8b00600.
  • Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, Hickey AJ, Clark AM. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater. 2019 2019/05/01;18(5):435–441. doi:10.1038/s41563-019-0338-z.
  • Bhhatarai B, Walters WP, Hop CECA, Lanza G, Ekins S. Opportunities and challenges using artificial intelligence in ADME/Tox. Nat Mater. 2019 2019/05/01;18(5):418–422. doi:10.1038/s41563-019-0332-5.
  • Walsh J, Gheorghe CE, Lyte JM, van de Wouw M, Boehme M, Dinan TG, et al. 2020. Gut microbiome-mediated modulation of hepatic cytochrome P450 and P-glycoprotein: impact of butyrate and fructo-oligosaccharide-inulin. Journal of Pharmacy and Pharmacology. 2020/04/26; 72: 1072-1081. https://doi.org/10.1111/jphp.13276.
  • Walsh J, Olavarria-Ramirez L, Lach G, Boehme M, Dinan TG, Cryan JF, Griffin BT, Hyland NP, Clarke G. Impact of host and environmental factors on β-glucuronidase enzymatic activity: implications for gastrointestinal serotonin. American Journal of Physiology-Gastrointestinal and Liver Physiology. 2020 2020/04/01;318(4):G816–G826. doi:10.1152/ajpgi.00026.2020.
  • Walsh J, Griffin BT, Clarke G, Hyland NP. Drug-gut microbiota interactions: implications for neuropharmacology. . British Journal of Pharmacology. 2018 Dec;;175(24):4415–4429. doi:10.1111/bph.14366.
  • Zimmermann-Kogadeeva M, Zimmermann M, Goodman AL. Insights from pharmacokinetic models of host-microbiome drug metabolism. Gut Microbes. 2019;11:3, 587-596, DOI:10.1080/19490976.2019.1667724.
  • Schuhmacher A, Gatto A, Hinder M, Kuss M, Gassmann O. The upside of being a digital pharma player. Drug Discov Today 2020 Jun 15.doi: 10.1016/j.drudis.2020.06.002.
  • Royal Society Working Group. Dynamics of data science skills: how can all sectors benefit from data science talent? United Kingdom; 2019. URL: https://royalsociety.org/topics-policy/projects/dynamics-of-data-science/.
  • Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et al. Clinical features of patients infected with 2019 novel coronavirus in wuhan, china [Article]. The Lancet. 2020;395(10223):497–506. doi:10.1016/S0140-6736(20)30183-5.
  • Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study [Article]. The Lancet. 2020;395(10223):507–513. doi:10.1016/S0140-6736(20)30211-7.
  • Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, et al. A novel coronavirus from patients with pneumonia in China, 2019 [Article]. New England Journal of Medicine. 2020;382(8):727–733. doi:10.1056/NEJMoa2001017.
  • COVID-19 to plunge global economy into worst recession since world war ii [internet]. the world bank; 2020; 8th June 2020. Available from:https://www.worldbank.org/en/news/press-release/2020/06/08/covid-19-to-plunge-global-economy-into-worst-recession-since-world-war-ii
  • Mak -K-K, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019 Mar;;24(3):773–780. doi:10.1016/j.drudis.2018.11.014.
  • Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science (New York, NY). 2019;363(6427):9931. doi:10.1126/science.aat9931.
  • Food and Agriculture Organization of the United Nations and World Health Organisation. Guidelines for the evaluation of probiotics in food. London Ontario (Canada); 2002. p. 1–11.
  • NuBiyota. safety and efficacy of met-3 in obese human subjects: clinicalTrials.gov; 2018 [accessed 3rd April 2020]. Available from: https://clinicaltrials.gov/ct2/show/NCT03660748?id=NCT03660748&draw=2&rank= 1
  • Zhao M, Shen C, Ma L. Treatment efficacy of probiotics on atopic dermatitis, zooming in on infants: a systematic review and meta-analysis. Int J Dermatol. 2018 2018/06/01;57(6):635–641. doi:10.1111/ijd.13873.
  • Gibson GR, Hutkins R, Sanders ME, Prescott SL, Reimer RA, Salminen SJ, Scott K, Stanton C, Swanson KS, Cani PD, et al. Expert consensus document: the international scientific association for probiotics and prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics [review]. Nat Rev Gastroenterol Hepatol. 2017;14(8):491–502. doi:10.1038/nrgastro.2017.75.
  • Hester SN, Mastaloudis A, Gray R, Antony JM, Evans M, Wood SM. Efficacy of an anthocyanin and prebiotic blend on intestinal environment in obese male and female subjects. Journal of Nutrition and Metabolism. 2018;2018:7497260. doi:10.1155/2018/7497260.
  • Bomhof MR, Parnell JA, Ramay HR, Crotty P, Rioux KP, Probert CS, Jayakumar S, Raman M, Reimer RA. Histological improvement of non-alcoholic steatohepatitis with a prebiotic: a pilot clinical trial [article]. Eur J Nutr. 2019;58(4):1735–1745. doi:10.1007/s00394-018-1721-2.
  • Chen PB, Black AS, Sobel AL, Zhao Y, Mukherjee P, Molparia B, Moore NE, Aleman Muench GR, Wu J, Chen W, et al. Directed remodeling of the mouse gut microbiome inhibits the development of atherosclerosis. Nat Biotechnol. 2020;38(11):1288–1297. 2020/06//. doi:10.1038/s41587-020-0549-5.
  • Brody LP, Sahuri-Arisoylu M, Parkinson JR, Parkes HG, So PW, Hajji N, et al. Cationic lipid-based nanoparticles mediate functional delivery of acetate to tumor cells in vivo leading to significant anticancer effects [article]. Int J Nanomedicine. 2017;12:6677–6685. doi:10.2147/IJN.S135968.
  • Mills S, Ross RP, Hill C. Bacteriocins and bacteriophage; a narrow-minded approach to food and gut microbiology [review]. FEMS Microbiol Rev. 2017;41(1):S129–S153. doi:10.1093/femsre/fux022.
  • Yadav V, Gaisford S, Merchant HA, Basit AW. Colonic bacterial metabolism of corticosteroids. Int J Pharm. 2013;457(1):268–274. 2013 Nov 30. doi:10.1016/j.ijpharm.2013.09.007.
  • Basit AW, Lacey LF. Colonic metabolism of ranitidine: implications for its delivery and absorption [article]. Int J Pharm. 2001;227(1–2):157–165. doi:10.1016/S0378-5173(01)00794-3.
  • Kim D-H. Gut microbiota-mediated drug-antibiotic interactions. Drug Metabolism and Disposition. 2015;43(10):1581. doi:10.1124/dmd.115.063867.
  • Basit AW, Newton JM, Lacey LF. Susceptibility of the H2-receptor antagonists cimetidine, famotidine and nizatidine, to metabolism by the gastrointestinal microflora. Int J Pharm. 2002 2002/04/26/;237(1–2):23–33. doi:10.1016/S0378-5173(02)00018-2.
  • Sousa T, Yadav V, Zann V, Borde A, Abrahamsson B, Basit AW. On the colonic bacterial metabolism of azo-bonded prodrugs of 5-aminosalicylic acid. J Pharm Sci. 2014;103(10):3171–3175. 2014/10/01. doi:10.1002/jps.24103.
  • Yadav V, Varum F, Bravo R, Furrer E, Basit AW. Gastrointestinal stability of therapeutic anti-TNF α IgG1 monoclonal antibodies. Int J Pharm. 2016 2016 Apr 11;502(1–2):181–187. doi:10.1016/j.ijpharm.2016.02.014.