407
Views
0
CrossRef citations to date
0
Altmetric
Review

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

, &
Pages 241-257 | Received 03 Jun 2022, Accepted 09 Jan 2023, Published online: 21 Jan 2023

References

  • Albalawi F, Hussein MZ, Fakurazi S, et al. Engineered nanomaterials: the challenges and opportunities for nanomedicines. Int J Nanomedicine. 2021 [cited 2021 Dec 20];16:161–184. doi: 10.2147/IJN.S288236.
  • Castro de KC, Costa JM, Campos MGN. Drug-loaded polymeric nanoparticles: a review. Int J Polym Mater Polym Biomater. 2020 [cited 2021 Oct 8];1–13. doi: 10.1080/00914037.2020.1798436.
  • Liu Y, Yang G, Jin S, et al. Development of high-drug-loading nanoparticles. ChemPlusChem. 2020 [cited 2021 Dec 6];85(9):2143–2157 doi:10.1002/cplu.202000496
  • Ventola CL. Progress in nanomedicine: approved and investigational nanodrugs. Pharm Ther. 2017 [cited 2021 Dec 11];42:742–755. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5720487/
  • Acharya S, Sahoo SK. PLGA nanoparticles containing various anticancer agents and tumour delivery by EPR effect. Adv Drug Deliv Rev. 2011 [cited 2021 Oct 14];63(3):170–183 doi:10.1016/j.addr.2010.10.008.
  • Bobo D, Robinson KJ, Islam J, et al. Nanoparticle-based medicines: a review of FDA-approved materials and clinical trials to date. Pharm Res. 2016 [cited 2021 Oct 8];33(10):2373–2387 doi:10.1007/s11095-016-1958-5.
  • Kang H, Rho S, Stiles WR, et al. Size-dependent EPR effect of polymeric nanoparticles on tumor targeting. Adv Healthc Mater. 2020 [cited 2021 Oct 14];9(1):1901223 doi:10.1002/adhm.201901223
  • Matsumura Y, Maeda H, New A. Concept for macromolecular therapeutics in cancer chemotherapy: mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs. Cancer Res. 1986 [cited 2021 Dec 17];46:6387–6392. Available from https://cancerres.aacrjournals.org/content/46/12_Part_1/6387
  • Sun D, Zhou S, Gao W. What went wrong with anticancer nanomedicine design and how to make it right. ACS Nano. 2020 [cited 2022 Jan 20];14(10):12281–12290 doi:10.1021/acsnano.9b09713.
  • Hare JI, Lammers T, Ashford MB, et al. Challenges and strategies in anti-cancer nanomedicine development: an industry perspective. Adv Drug Deliv Rev. 2017 [cited 2021 Dec 8];108:25–38. doi: 10.1016/j.addr.2016.04.025
  • Etheridge ML, Campbell SA, Erdman AG, et al. The big picture on nanomedicine: the state of investigational and approved nanomedicine products. Nanomed Nanotechnol Biol Med. 2013 [cited 2021 Dec 18];9(1):1–14 doi:10.1016/j.nano.2012.05.013
  • Lopez-Cantu DO, Wang X, Carrasco-Magallanes H, et al. From bench to the clinic: the path to translation of nanotechnology-enabled mRNA SARS-CoV-2 vaccines. Nano-Micro Lett. 2022 [cited 2022 Jul 16];14(1):41 doi:10.1007/s40820-021-00771-8.
  • Roser M, Ortiz-Ospina E. COVID-19 vaccine doses administered by manufacturer [Internet]. Our World Data. cited 2022 May 10 Available from. https://ourworldindata.org/grapher/covid-vaccine-doses-by-manufacturer
  • Varahachalam SP, Lahooti B, Chamaneh M, et al. Nanomedicine for the SARS-CoV-2: state-of-the-Art and Future Prospects. Int J Nanomedicine. 2021 [cited 2022 Jul 15];16:539–560. doi: 10.2147/IJN.S283686.
  • Tang Z, Kong N, Zhang X, et al. A materials-science perspective on tackling COVID-19. Nat Rev Mater. 2020 [cited 2022 Jul 16];5(11):847–860 doi:10.1038/s41578-020-00247-y
  • Tang Z, Zhang X, Shu Y, et al. Insights from nanotechnology in COVID-19 treatment. Nano Today. 2021 [cited 2022 Jul 16];36:101019. doi: 10.1016/j.nantod.2020.101019
  • Wang X, Hu T, Hu B, et al. Imparting reusable and SARS-CoV-2 inhibition properties to standard masks through metal-organic nanocoatings. J Hazard Mater. 2022 [cited 2022 Jul 16];431:128441. doi: 10.1016/j.jhazmat.2022.128441
  • Farjadian F, Ghasemi A, Gohari O, et al. Nanopharmaceuticals and nanomedicines currently on the market: challenges and opportunities. Nanomed. 2019 [cited 2022 Mar 14];14(1):93–126 doi:10.2217/nnm-2018-0120
  • Global Industry Analysts, Inc. Healthcare nanotechnology (Nanomedicine) - global market trajectory & analytics [Internet]. 2021. [cited 2022 Jan 2]. Available from: https://www.researchandmarkets.com/reports/5030234/healthcare-nanotechnology-nanomedicine-global.
  • Anselmo AC, Mitragotri S. Nanoparticles in the clinic: an update. Bioeng Transl Med. 2019 [cited 2021 Oct 8];4(3):e10143 doi:10.1002/btm2.10143.
  • Mitchell MJ, Billingsley MM, Haley RM, et al. Engineering precision nanoparticles for drug delivery. Nat Rev Drug Discov. 2021 [cited 2021 Dec 1];20(2):101–124 doi:10.1038/s41573-020-0090-8
  • Siepmann J, Faham A, Clas S-D, et al. Lipids and polymers in pharmaceutical technology: lifelong companions. Int J Pharm. 2019 [cited 2021 Dec 12];558:128–142. doi: 10.1016/j.ijpharm.2018.12.080
  • Tenchov R, Bird R, Curtze AE, et al. Lipid nanoparticles─from liposomes to mRNA vaccine delivery, a landscape of research diversity and advancement. ACS Nano. 2021 [cited 2022 Jan 19];15(11):16982–17015 doi:10.1021/acsnano.1c04996.
  • Pharmacoeconomic review report: patisiran (Onpattro): (Alnylam Netherlands B.V.): indication: treatment of polyneuropathy in adult patients with hereditary transthyretin-mediated amyloidosis [Internet]. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2019 [cited 2021 Dec 14]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK549694/.
  • Dyer O. Covid-19: countries are learning what others paid for vaccines. BMJ. 2021 [cited 2021 Dec 14];372:n281. doi: 10.1136/bmj.n281
  • Wang W, Ye Z, Gao H, et al. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release. 2021 [cited 2022 Mar 16];338:119–136. doi: 10.1016/j.jconrel.2021.08.030
  • Leroux J. Editorial: drug delivery: too much complexity, not enough reproducibility? Angew Chem Int Ed. 2017 [cited 2021 Dec 9];56(48):15170–15171 doi:10.1002/anie.201709002.
  • Dirnagl U, Duda GN, Grainger DW, et al. Reproducibility, relevance and reliability as barriers to efficient and credible biomedical technology translation. Adv Drug Deliv Rev. 2022 [cited 2022 Feb 24];182:114118. doi: 10.1016/j.addr.2022.114118.
  • Lammers T, Storm G. Setting standards to promote progress in bio–nano science. Nat Nanotechnol. 2019 [cited 2021 Dec 8];14(7):626 doi:10.1038/s41565-019-0497-8.
  • Steiner S, Wolf J, Glatzel S, et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science. 2019 [cited 2022 Mar 13];363(6423):eaav2211 doi:10.1126/science.aav2211
  • Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell. 2020 [cited 2022 Mar 13;]180(4):688–702.e13 doi:10.1016/j.cell.2020.01.021
  • Yang Y, Ye Z, Su Y, et al. Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm Sin B. 2019 [cited 2021 Dec 22];9(1):177–185 doi:10.1016/j.apsb.2018.09.010
  • Operti MC, Bernhardt A, Grimm S, et al. PLGA-based nanomedicines manufacturing: technologies overview and challenges in industrial scale-up. Int J Pharm. 2021 [cited 2021 Oct 26];605:120807. doi: 10.1016/j.ijpharm.2021.120807
  • Ghitman J, Biru EI, Stan R, et al. Review of hybrid PLGA nanoparticles: future of smart drug delivery and theranostics medicine. Mater Des. 2020 [cited 2021 Dec 22];193:108805. doi: 10.1016/j.matdes.2020.108805
  • Kumar JN, Li Q, Jun Y. Challenges and opportunities of polymer design with machine learning and high throughput experimentation. MRS Commun. 2019 [cited 2021 Dec 14];9(2):537–544 doi:10.1557/mrc.2019.54.
  • Sharma S, Parmar A, Kori S, et al. PLGA-based nanoparticles: a new paradigm in biomedical applications. TRAC-Trends Anal Chem. 2016 [cited 2022 Mar 4];80:30–40. doi: 10.1016/j.trac.2015.06.014
  • Choi J-S, Park J-S. Design and evaluation of the anticancer activity of paclitaxel-loaded anisotropic-poly(lactic-co-glycolic acid) nanoparticles with PEGylated chitosan surface modifications. Int J Biol Macromol. 2020 [cited 2021 Dec 15];162:1064–1075. doi: 10.1016/j.ijbiomac.2020.06.237
  • Luiz MT, Abriata JP, Raspantini GL, et al. In vitro evaluation of folate-modified PLGA nanoparticles containing paclitaxel for ovarian cancer therapy. Mater Sci Eng C. 2019 [cited 2021 Dec 15];105:110038. doi: 10.1016/j.msec.2019.110038
  • Kou L, Hou Y, Yao Q, et al. L-Carnitine-conjugated nanoparticles to promote permeation across blood–brain barrier and to target glioma cells for drug delivery via the novel organic cation/carnitine transporter OCTN2. Artif Cells Nanomed Biotechnol. 2018 [cited 2021 Dec 23];46:1605–1616. doi: 10.1080/21691401.2017.1384385
  • Jeevarathinam AS, Lemaster JE, Chen F, et al. Photoacoustic imaging quantifies drug release from nanocarriers via redox chemistry of dye-labeled cargo. Angew Chem Int Ed Engl. 2020 [cited 2021 Dec 15];59(12):4678–4683 doi:10.1002/anie.201914120
  • Duan T, Xu Z, Sun F, et al. HPA aptamer functionalized paclitaxel-loaded PLGA nanoparticles for enhanced anticancer therapy through targeted effects and microenvironment modulation. Biomed Pharmacother. 2019 [cited 2021 Dec 15];117:109121. doi: 10.1016/j.biopha.2019.109121
  • Cerqueira BBS, Lasham A, Shelling AN, et al. Development of biodegradable PLGA nanoparticles surface engineered with hyaluronic acid for targeted delivery of paclitaxel to triple negative breast cancer cells. Mater Sci Eng C. 2017 [cited 2022 Mar 3];76:593–600. doi: 10.1016/j.msec.2017.03.121
  • Thi TTH, Suys EJA, Lee JS, et al. Lipid-based nanoparticles in the clinic and clinical trials: from cancer nanomedicine to COVID-19 Vaccines. Vaccines. 2021 [cited 2022 Feb 2];9(4):359 doi:10.3390/vaccines9040359
  • Bulbake U, Doppalapudi S, Kommineni N, et al. Liposomal formulations in clinical use: an updated review. Pharmaceutics. 2017 [cited 2022 Feb 2];9(4):12 doi:10.3390/pharmaceutics9020012
  • Carrasco MJ, Alishetty S, Alameh M-G, et al. Ionization and structural properties of mRNA lipid nanoparticles influence expression in intramuscular and intravascular administration. Commun Biol. 2021 [cited 2022 Apr 23];4(1):1–15 doi:10.1038/s42003-021-02441-2
  • Baptista B, Carapito R, Laroui N, et al. mRNA, a revolution in biomedicine. Pharmaceutics. 2021 [cited 2022 Apr 23];13(12):2090 doi:10.3390/pharmaceutics13122090
  • Anselmo AC, Mitragotri S. Nanoparticles in the clinic. Bioeng Transl Med. 2016 [cited 2021 Oct 8];1(1):10–29 doi:10.1002/btm2.10003.
  • Merle P, Blanc J-F, Phelip J-M, et al. Doxorubicin-loaded nanoparticles for patients with advanced hepatocellular carcinoma after sorafenib treatment failure (RELIVE): a phase 3 randomised controlled trial. Lancet Gastroenterol Hepatol. 2019 [cited 2022 Jan 22];4(6):454–465 doi:10.1016/S2468-1253(19)30040-8.
  • Hou X, Zaks T, Langer R, et al. Lipid nanoparticles for mRNA delivery. Nat Rev Mater. 2021 [cited 2021 Oct 28];1–17 . https://www.nature.com/articles/s41578-021-00358-0
  • Christensen M, Yunker LPE, Shiri P, et al. Automation isn’t automatic. Chem Sci. 2021 [cited 2021 Dec 7]; 12(47):15473–15490 doi:10.1039/D1SC04588A.
  • Egorov E, Pieters C, Korach-Rechtman H, et al. Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems. Drug Deliv Transl Res. 2021 [cited 2021 Dec 6];11(2):345–352 doi:10.1007/s13346-021-00929-2.
  • Kong F, Yuan L, Zheng YF, et al. Automatic liquid handling for life science: a critical review of the current state of the Art. J Lab Autom. 2012 [cited 2022 Jan 20];17(3):169–185 doi:10.1177/2211068211435302.
  • Lehmann R, Severitt JC, Roddelkopf T, et al. Biomek cell workstation: a variable system for automated cell cultivation. J Lab Autom. 2016 [cited 2022 Jan 20];21(3):439–450. doi: 10.1177/2211068215599786
  • Shiri P, Lai V, Zepel T, et al. Automated solubility screening platform using computer vision. iScience. 2021 [cited 2022 Sep 29];24(3):102176 doi:10.1016/j.isci.2021.102176
  • Eppel S, Xu H, Ru Wang Y, et al. Predicting 3D shapes, masks, and properties of materials inside transparent containers, using the TransProteus CGI dataset. Digit Discov. 2022 [cited 2022 Sep 29];1(1):45–60 doi:10.1039/D1DD00014D.
  • Fan Y, Yen C-W, Lin H-C, et al. Automated high-throughput preparation and characterization of oligonucleotide-loaded lipid nanoparticles. Int J Pharm. 2021 [cited 2021 Sep 2];599:120392. doi: 10.1016/j.ijpharm.2021.120392
  • Sarode A, Fan Y, Byrnes AE, et al. Predictive high-throughput screening of PEGylated lipids in oligonucleotide-loaded lipid nanoparticles for neuronal gene silencing. Nanoscale Adv. 2022 [cited 2022 Sep 20];4(9):2107–2123 doi:10.1039/D1NA00712B
  • Cui L, Pereira S, Sonzini S, et al. Development of a high-throughput platform for screening lipid nanoparticles for mRNA delivery. Nanoscale. 2022 [cited 2022 Sep 20;]14(4):1480–1491 doi:10.1039/D1NR06858J
  • Fan Y, Shi Z, Ma S, et al. Spectroscopy-based local modeling method for high-throughput quantification of nucleic acid loading in lipid nanoparticles. Anal Chem. 2022 [cited 2022 Sep 20];94(25):9081–9090 doi:10.1021/acs.analchem.2c01346.
  • Ebeid K, Meng X, Thiel KW, et al. Synthetically lethal nanoparticles for treatment of endometrial cancer. Nat Nanotechnol. 2018 [cited 2021 Dec 15];13(1):72–81 doi:10.1038/s41565-017-0009-7
  • Fernandes E, Ferreira D, Peixoto A, et al. Glycoengineered nanoparticles enhance the delivery of 5-fluoroucil and paclitaxel to gastric cancer cells of high metastatic potential. Int J Pharm. 2019 [cited 2021 Dec 15];570:118646. doi: 10.1016/j.ijpharm.2019.118646
  • Wang X, Yang L, Zhang H, et al. Fluorescent magnetic PEI-PLGA nanoparticles loaded with paclitaxel for concurrent cell imaging, enhanced apoptosis and autophagy in human brain cancer. Colloids Surf B Biointerfaces. 2018 [cited 2021 Dec 15];172:708–717. doi: 10.1016/j.colsurfb.2018.09.033
  • Roces CB, Lou G, Jain N, et al. Manufacturing considerations for the development of lipid nanoparticles using microfluidics. Pharmaceutics. 2020 [cited 2021 Dec 26];12(11):1095 doi:10.3390/pharmaceutics12111095
  • Vu HTH, Streck S, Hook SM, et al. Utilization of microfluidics for the preparation of polymeric nanoparticles for the antioxidant rutin: a comparison with bulk production. Pharm Nanotechnol. 2019;7(6):469–483 doi:10.2174/2211738507666191019141049.
  • Liu L, Bi M, Wang Y, et al. Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis. Nanoscale. 2021 [cited 2022 Jul 15];13(46):19352–19366 doi:10.1039/D1NR06195J.
  • Streck S, Neumann H, Nielsen HM, et al. Comparison of bulk and microfluidics methods for the formulation of poly-lactic-co-glycolic acid (PLGA) nanoparticles modified with cell-penetrating peptides of different architectures. Int J Pharm X. 2019 [cited 2021 Dec 8];1:100030 doi:10.1016/j.ijpx.2019.100030.
  • Bovone G, Steiner F, Guzzi EA, et al. Automated and continuous production of polymeric nanoparticles. Front Bioeng Biotechnol. 2019 [cited 2021 Nov 9];7:423. doi: 10.3389/fbioe.2019.00423
  • Tao H, Wu T, Kheiri S, et al. Self-driving platform for metal nanoparticle synthesis: combining microfluidics and machine learning. Adv Funct Mater. 2021 [cited 2022 Mar 6];31(51):2106725 doi:10.1002/adfm.202106725.
  • Loy DM, Krzysztoń R, Lächelt U, et al. Controlling nanoparticle formulation: a low-budget prototype for the automation of a microfluidic platform. Processes. 2021 [cited 2021 Dec 7];9(1):129 doi:10.3390/pr9010129
  • Tao H, Wu T, Aldeghi M, et al. Nanoparticle synthesis assisted by machine learning. Nat Rev Mater. 2021 [cited 2021 Sep 2];6(8):701–716 doi:10.1038/s41578-021-00337-5.
  • Bannigan P, Aldeghi M, Bao Z, et al. Machine learning directed drug formulation development. Adv Drug Deliv Rev. 2021 [cited 2021 Aug 31];175:113806. doi: 10.1016/j.addr.2021.05.016.
  • Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018 [cited 2021 Dec 30];15(4):233–234 doi:10.1038/nmeth.4642.
  • Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019 [cited 2021 Sep 4;]18(6):463–477 doi:10.1038/s41573-019-0024-5
  • Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021 [cited 2022 Jan 20];2(3):160 doi:10.1007/s42979-021-00592-x.
  • Damiati SA. Digital Pharmaceutical Sciences. AAPS PharmSciTech. 2020 [cited 2022 Mar 16];21(6):206 doi:10.1208/s12249-020-01747-4.
  • Häse F, Roch LM, Aspuru-Guzik A. Next-generation experimentation with self-driving laboratories. Trends Chem. 2019 [cited 2022 Feb 2];1(3):282–291 doi:10.1016/j.trechm.2019.02.007.
  • Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design - A review. Curr Top Med Chem. 2010;10(1):95–115.
  • Suay-García B, Bueso-Bordils JI, Falcó A, et al. Virtual combinatorial chemistry and pharmacological screening: a short guide to drug design. Int J Mol Sci. 2022 [cited 2022 Mar 26];23(3):1620 doi:10.3390/ijms23031620
  • Mao J, Akhtar J, Zhang X, et al. Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience. 2021 [cited 2022 May 2];24(9):103052 doi:10.1016/j.isci.2021.103052.
  • Tsou LK, Yeh S-H, Ueng S-H, et al. Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery. Sci Rep. 2020 [cited 2022 May 2];10(1):16771 doi:10.1038/s41598-020-73681-1
  • Chakravarti SK, Alla SRM. Descriptor free QSAR modeling using deep learning with long short-term memory neural networks. Front Artif Intell. 2019. [cited 2022 May 7];2 17 doi:10.3389/frai.2019.00017 7 May. .
  • Sason H, Shamay Y. Nanoinformatics in drug delivery. Isr J Chem. 2020 [cited 2022 Jul 24];60(12):1108–1117 doi:10.1002/ijch.201900042.
  • Jones DE, Ghandehari H, Facelli JC. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles. Comput Methods Programs Biomed. 2016 [cited 2021 Dec 14];132:93–103. doi: 10.1016/j.cmpb.2016.04.025
  • Singh AV, Rosenkranz D, Ansari MHD, et al. Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction. Adv Intell Syst. 2020 [cited 2022 Mar 6];2(12):2000084 doi:10.1002/aisy.202000084
  • Youshia J, Ali ME, Lamprecht A. Artificial neural network based particle size prediction of polymeric nanoparticles. Eur J Pharm Biopharm. 2017 [cited 2021 Dec 29];119:333–342. doi: 10.1016/j.ejpb.2017.06.030
  • Asadi H, Rostamizadeh K, Salari D, et al. Preparation of biodegradable nanoparticles of tri-block PLA––PEG––PLA copolymer and determination of factors controlling the particle size using artificial neural network. J Microencapsul. 2011 [cited 2021 Dec 14];28(5):406–416 doi:10.3109/02652048.2011.576784
  • He Y, Ye Z, Liu X, et al. Can machine learning predict drug nanocrystals? J Control Release. 2020 [cited 2022 Mar 6];322:274–285. doi: 10.1016/j.jconrel.2020.03.043
  • Gao H, Wang W, Dong J, et al. An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design. Eur J Pharm Biopharm. 2021 [cited 2022 Mar 10];158:336–346. doi: 10.1016/j.ejpb.2020.12.001
  • Cai C, Wang S, Xu Y, et al. Transfer learning for drug discovery. J Med Chem. 2020 [cited 2022 Sep 29];63(16):8683–8694 doi:10.1021/acs.jmedchem.9b02147.
  • Huang L, Ling C. Leveraging transfer learning and chemical principles toward interpretable materials properties. J Chem Inf Model. 2021 [cited 2022 Sep 29];61(9):4200–4209 doi:10.1021/acs.jcim.1c00434.
  • Smith JS, Nebgen BT, Zubatyuk R, et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat Commun. 2019 [ited 2022 Sep 29];10(1):2903 doi:10.1038/s41467-019-10827-4
  • Wiercioch M, Kirchmair J. Dealing with a data-limited regime: combining transfer learning and transformer attention mechanism to increase aqueous solubility prediction performance. Artif Intell Life Sci. 2021 [cited 2021 Dec 7];1:100021 doi:10.1016/j.ailsci.2021.100021.
  • Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: generative models for matter engineering. Science. 2018 [cited 2021 Oct 14];361(6400):360–365 doi:10.1126/science.aat2663.
  • Kusne AG, Yu H, Wu C, et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat Commun. 2020 [cited 2022 Jan 3];11(1):5966 doi:10.1038/s41467-020-19597-w
  • Kumar JN, Li Q, Tang KYT, et al. Machine learning enables polymer cloud-point engineering via inverse design. Npj Comput Mater. 2019 [cited 2021 Dec 14];5(1):1–6 doi:10.1038/s41524-019-0209-9
  • Mekki-Berrada F, Ren Z, Huang T, et al. Two-step machine learning enables optimized nanoparticle synthesis. Npj Comput Mater. 2021 [cited 2021 Dec 14];7(1):1–10 doi:10.1038/s41524-021-00520-w
  • Kumar R, Le N, Tan Z, et al. Efficient polymer-mediated delivery of gene-editing ribonucleoprotein payloads through combinatorial design, parallelized experimentation, and machine learning. ACS Nano. 2020;14(12):17626–17639 doi:10.1021/acsnano.0c08549.
  • Yamankurt G, Berns EJ, Xue A, et al. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nat Biomed Eng. 2019 [cited 2021 Dec 29];3(4):318–327 doi:10.1038/s41551-019-0351-1
  • Stein H S, Gregoire J M. Progress and prospects for accelerating materials science with automated and autonomous workflows. Chem Sci.. 2019 [cited 2022 Sep 29];10(42):9640–9649 doi:10.1039/C9SC03766G.
  • Stach E, DeCost B, Kusne AG, et al. Autonomous experimentation systems for materials development: a community perspective. Matter. 2021 [cited 2022 Sep 29];4(9):2702–2726 doi:10.1016/j.matt.2021.06.036
  • Nambiar AMK, Breen CP, Hart T, et al. Bayesian optimization of computer-proposed multistep synthetic routes on an automated robotic flow platform. ACS Cent Sci. 2022 [cited 2022 Sep 29];8(6):825–836 doi:10.1021/acscentsci.2c00207.
  • Shields BJ, Stevens J, Li J, et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature. 2021 [cited 2022 Sep 29];590(7844):89–96 doi:10.1038/s41586-021-03213-y
  • Baumgartner LM, Coley CW, Reizman BJ, et al. Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform. React Chem Eng. 2018 [cited 2022 Sep 29];3(3):301–311 doi:10.1039/C8RE00032H
  • Cortés-Borda D, Wimmer E, Gouilleux B, et al. An autonomous self-optimizing flow reactor for the synthesis of natural product carpanone. J Org Chem. 2018 [cited 2022 Sep 29];83(23):14286–14299 doi:10.1021/acs.joc.8b01821.
  • Xue D, Balachandran PV, Hogden J, et al. Accelerated search for materials with targeted properties by adaptive design. Nat Commun. 2016 [cited 2022 Sep 29];7(1):11241 doi:10.1038/ncomms11241
  • Nikolaev P, Hooper D, Webber F, et al. Autonomy in materials research: a case study in carbon nanotube growth. Npj Comput Mater. 2016 [cited 2022 Sep 29];2(1):1–6 doi:10.1038/npjcompumats.2016.31
  • Rohr B, Stein HS, Guevarra D, et al. Benchmarking the acceleration of materials discovery by sequential learning. Chem Sci. 2020 [cited 2022 Sep 29];11(10):2696–2706 doi:10.1039/C9SC05999G
  • Burger B, Maffettone PM, Gusev VV, et al. A mobile robotic chemist. Nature. 2020 [cited 2022 Sep 29];583(7815):237–241 doi:10.1038/s41586-020-2442-2
  • Crabtree G. Self-driving laboratories coming of age. Joule. 2020 [cited 2022 Feb 2];4(12):2538–2541 doi:10.1016/j.joule.2020.11.021.
  • Bian Y, Xie X-Q. Generative chemistry: drug discovery with deep learning generative models. J Mol Model. 2021 [cited 2022 Feb 3];27(3):71 doi:10.1007/s00894-021-04674-8.
  • Butakova MA, Chernov AV, Kartashov OO, et al. Data-centric architecture for self-driving laboratories with autonomous discovery of new nanomaterials. Nanomaterials. 2021 [cited 2022 Feb 2];12(1):12 doi:10.3390/nano12010012
  • Tamasi MJ, Patel RA, Borca CH, et al. Machine learning on a robotic platform for the design of polymer–protein hybrids. Adv Mater. 2022 [cited 2022 Sep 22];34(30):2201809 doi:10.1002/adma.202201809.
  • Dalal R, Leyden M, Oviedo F, et al. Polymer design via SHAP and bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery [Internet]. Research Square; 2022 [cited 2022 Sep 20]. Available from: https://www.researchsquare.com/article/rs-1785891/v1.
  • Wang S, Shen Z, Shen Z, et al. Machine-learning micropattern manufacturing. Nano Today. 2021 [cited 2022 Jul 16];38:101152. doi:10.1016/j.nantod.2021.101152
  • Villa Nova M, Lin TP, Shanehsazzadeh S, et al. Nanomedicine ex machina: between model-informed development and artificial intelligence. Front Digit Health. 2022 [cited 2022 Apr 10];4:799341. doi: 10.3389/fdgth.2022.799341
  • Serov N, Vinogradov V. Artificial intelligence to bring nanomedicine to life. Adv Drug Deliv Rev. 2022 [cited 2022 Jul 24];184:114194. doi:10.1016/j.addr.2022.114194
  • Chen C, Yaari Z, Apfelbaum E, et al. Merging data curation and machine learning to improve nanomedicines. Adv Drug Deliv Rev . 2022 [cited 2022 Jul 24];183:114172. doi: 10.1016/j.addr.2022.114172
  • Ong JJ, Castro BM, Gaisford S, et al. Accelerating 3D printing of pharmaceutical products using machine learning. Int J Pharm X. 2022 [cited 2022 Sep 20];4:100120. doi: 10.1016/j.ijpx.2022.100120
  • Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering. 2019 [cited 2021 Dec 14];5(6):1017–1026. doi:10.1016/j.eng.2019.02.011
  • Hayat H, Nukala A, Nyamira A, et al. A concise review: the synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine. Biomed Mater. 2021 [cited 2022 Jul 24];16(5):052001. doi:10.1088/1748-605X/ac15b2
  • Singh AV, Ansari MHD, Rosenkranz D, et al. Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Adv Healthc Mater. 2020 [cited 2022 Mar 6];9(17):1901862. doi:10.1002/adhm.201901862
  • Singh AV, Maharjan R-S, Kanase A, et al. Machine-learning-based approach to decode the influence of nanomaterial properties on their interaction with cells. ACS Appl Mater Interfaces. 2021 [cited 2022 Jul 24];13(1):1943–1955. doi:10.1021/acsami.0c18470
  • Jia Y, Hou X, Wang Z, et al. Machine learning boosts the design and discovery of nanomaterials. ACS Sustain Chem Eng. 2021 [cited 2022 Jul 29];9(18):6130–6147. doi:10.1021/acssuschemeng.1c00483
  • Zhang C, Mou M, Zhou Y, et al. Biological activities of drug inactive ingredients. Brief Bioinform. 2022 [cited 2022 Jun 6];23(5):bbac160. doi:10.1093/bib/bbac160
  • Rebollo R, Oyoun F, Corvis Y, et al. Microfluidic manufacturing of liposomes: development and optimization by design of experiment and machine learning. ACS Appl Mater Interfaces. 2022 [cited 2022 Sep 20];14(35):39736–39745. doi:10.1021/acsami.2c06627
  • Schmitt JM, Baumann JM, Morgen MM. Predicting spray dried dispersion particle size via machine learning regression methods. Pharm Res. 2022 [cited 2022 Sep 20];39(12):3223–3239. doi:10.1007/s11095-022-03370-3
  • Yoo S, Kim J, Choi GJ. Drug properties prediction based on deep learning. Pharmaceutics. 2022 [cited 2022 Jun 6];14(2):467. doi:10.3390/pharmaceutics14020467
  • Basso J, Mendes M, Cova T, et al. A stepwise framework for the systematic development of lipid nanoparticles. Biomolecules. 2022 [cited 2022 Jun 6];12(2):223. doi:10.3390/biom12020223
  • Li J, Gao H, Ye Z, et al. In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques. Carbohydr Polym . 2022 [cited 2022 Mar 6];275:118712. doi: 10.1016/j.carbpol.2021.118712
  • Liu W, Zhao R, Su X, et al. Development and validation of machine learning models for prediction of nanomedicine solubility in supercritical solvent for advanced pharmaceutical manufacturing. J Mol Liq . 2022 [cited 2022 Jul 24];358:119208. doi: 10.1016/j.molliq.2022.119208
  • Lin Z, Chou W-C, Cheng Y-H, et al. Predicting nanoparticle delivery to tumors using machine learning and artificial intelligence approaches. Int J Nanomedicine . 2022 [cited 2022 Jul 24];17:1365–1379. doi: 10.2147/IJN.S344208
  • Wang W, Feng S, Ye Z, et al. Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm. Acta Pharm Sin B. 2022 [cited 2022 Jul 24];12(6):2950–2962. doi:10.1016/j.apsb.2021.11.021
  • Gao H, Kan S, Ye Z, et al. Development of in silico methodology for siRNA lipid nanoparticle formulations. Chem Eng J. 2022 [cited 2022 Jul 24];442:136310. doi: 10.1016/j.cej.2022.136310
  • Başkor A, Tok YP, Mesut B, et al. Estimating the optimal dexketoprofen pharmaceutical formulation with machine learning methods and statistical approaches. Healthc Inform Res. 2021 [c]ited 2022 Jun 22;27(4):279–286 doi:10.4258/hir.2021.27.4.279
  • Ye Z, Ouyang D. Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms. J Cheminformatics. 2021 [cited 2022 Jul 24];13(1):98 doi:10.1186/s13321-021-00575-3
  • Dong J, Gao H, Ouyang D. PharmSD: a novel AI-based computational platform for solid dispersion formulation design. Int J Pharm. 2021 [cited 2022 Mar 16];604 doi:10.1016/j.ijpharm.2021.120705
  • Hathout RM, Mahmoud OA, Ali DS, et al. Modeling drugs-PLGA nanoparticles interactions using gaussian processes: pharmaceutics informatics approach. J Clust Sci. 2021 [cited 2021 Dec 30]. doi: 10.1007/s10876-021-02126-0
  • Duan Y, Coreas R, Liu Y, et al. Prediction of protein Corona on nanomaterials by machine learning using novel descriptors. NanoImpact. 2020 [cited 2022 Jul 29];17:100207. doi: 10.1016/j.impact.2020.100207
  • Ban Z, Yuan P, Yu F, et al. Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles. Proc Natl Acad Sci. 2020 [cited 2022 Aug 29];117(19):10492–10499 doi:10.1073/pnas.1919755117
  • Gill J, Moullet M, Martinsson A, et al. Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction. CPT Pharmacomet Syst Pharmacol. [cited 2022 Dec 3];11(12):1560–1568 doi:10.1002/psp4.12870

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.