Pharmaceutical advances: Integrating artificial intelligence in QSAR, combinatorial and green chemistry practices

Baljit Singh, Michelle Crasto, Kamna Ravi, Sargun Singh

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Intelligent Pharmacy ›› 2024, Vol. 2 ›› Issue (5) : 598-608. DOI: 10.1016/j.ipha.2024.05.005
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Pharmaceutical advances: Integrating artificial intelligence in QSAR, combinatorial and green chemistry practices

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Abstract

The utilization of pharmaceuticals in medical and veterinary treatment has not only improved human and animal health but has also boosted food-production and economic welfare. However, the release of pharmaceuticals in the environment through various pathways, such as manufacturing, human excretion, and substandard disposal, can have detrimental effects on ecosystems and various biological entities associated with these systems. High levels of pharmaceutical residues have been detected further downstream of manufacturing facilities, and untreated veterinary medication leftovers can end up in waterbodies. Methods utilizing artificial intelligence (AI) and machine learning (ML) have been employed to establish connections between chemical structure and biological activity, referred to as quantitative structure–activity relationships (QSARs) for the compounds. QSAR models use chemical structures to predict hazardous activity when experimental data is lacking, thereby helping prioritize chemicals for testing and compilation. Combinatorial chemistry, by enabling high-throughput compound synthesis, accelerates the generation of targeted molecules for testing across various fields. Green chemistry helps in creating, designing, and implementing chemical products and procedures with the aim of minimizing or eradicating the generation and subsequent utilization of harmful substances. In addition, pharmaceutical sensor technologies (PST) are critical tools in modern medicine, enabling precise detection and monitoring of various biochemical and physiological markers and parameters. The synergy between AI, ML, QSAR modeling, and the implementation of combinatorial and green chemistry methodologies is pivotal in driving the development of innovative products and PST in pharmaceutics. This interdisciplinary approach is crucial for creating solutions with reduced toxicity in pharmaceutical processes, thereby ensuring enhanced public safety and promoting the sustainability of environmental resources. By integrating these advanced methodologies, the pharmaceutical industry can achieve greater detection accuracy, efficiency in production of eco-friendly products, ultimately leading to safer pharmaceutics and a healthier planet.

Keywords

Pharmaceuticals / Active pharmaceutical ingredients (APIs) / Artificial intelligence (AI) / QSAR / Combinatorial / Green chemistry / Environmental sustainability / Sensor technologies

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Baljit Singh, Michelle Crasto, Kamna Ravi, Sargun Singh. Pharmaceutical advances: Integrating artificial intelligence in QSAR, combinatorial and green chemistry practices. Intelligent Pharmacy, 2024, 2(5): 598‒608 https://doi.org/10.1016/j.ipha.2024.05.005

References

[1]
OECD. Pharmaceutical Residues in Freshwater: Hazards and Policy Responses, OECD Studies on Water. Paris: OECD Publishing;2019. https://doi.org/10.1787/c936f42den.
[2]
Ågerstrand M, Berg C, Bjöorlenius B, et al. Improving environmental risk assessment of human pharmaceuticals. Environ Sci Technol. 2015;49(9):5336–5345.
CrossRef Google scholar
[3]
Environmentally Persistent Pharmaceutical Pollutants (EPPPs) | UNEP - UN Environment Programme [Online]. Available: https://www.unep.org/explore-topics/chemicals-waste/what-we-do/emerging-issues/environmentally-persistent-pharmaceutical. Accessed April 30, 2024.
[4]
Price OR, Hughes GO, Roche NL, Mason PJ. Improving emissions estimates of home and personal care products ingredients for use in EU risk assessments. Integrated Environ Assess Manag. 2010;6(4):677–684.
CrossRef Google scholar
[5]
Boxall AB, Rudd MA, Brooks BW, et al. Pharmaceuticals and personal care products in the environment: what are the big questions? Environ Health Perspect. 2012;120(9):1221–1229.
CrossRef Google scholar
[6]
Oldenkamp R, Hamers T, Wilkinson J, Slootweg J, Posthuma L. Regulatory risk assessment of pharmaceuticals in the environment: current practice and future priorities. Environ Toxicol Chem. 2024;43(3):611–622.
CrossRef Google scholar
[7]
Adeel M, Song X, Wang Y, Francis D, Yang Y. Environmental impact of estrogens on human, animal and plant life: a critical review. Environ Int. 2017;99:107–119.
CrossRef Google scholar
[8]
Zainab SM, Junaid M, Xu N, Malik RN. Antibiotics and antibiotic resistant genes (ARGs) in groundwater: a global review on dissemination, sources, interactions, environmental and human health risks. Water Res. 2020;187:116455.
CrossRef Google scholar
[9]
Zhang C, Chen Y, Chen S, Guan X, Zhong Y, Yang Q. Occurrence, risk assessment, and in vitro and in vivo toxicity of antibiotics in surface water in China. Ecotoxicol Environ Saf. 2023;255:114817.
CrossRef Google scholar
[10]
Castillo-Zacarías C, Barocio ME, Hidalgo-Vázquez E, et al. Antidepressant drugs as emerging contaminants: occurrence in urban and non-urban waters and analytical methods for their detection. Sci Total Environ. 2021;757:143722.
CrossRef Google scholar
[11]
Chan SJ, Nutting VI, Natterson TA, Horowitz BN. Impacts of psychopharmaceuticals on the neurodevelopment of aquatic wildlife: a call for increased knowledge exchange across disciplines to highlight implications for human health. Int J Environ Res Publ Health. 2021;18(10):5094.
CrossRef Google scholar
[12]
Tyumina EA, Bazhutin GA, Cartagena Gómez ADP, Ivshina IB. Nonsteroidal anti-inflammatory drugs as emerging contaminants. Microbiology. 2020;89:148–163.
CrossRef Google scholar
[13]
Gini G, Zanoli F. Machine learning and deep learning methods in ecotoxicological QSAR modeling. Ecotoxicological QSARs. 2020:111–149.
CrossRef Google scholar
[14]
Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery. Pharmaceuticals. 2023;16(9):1259.
CrossRef Google scholar
[15]
Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023;15(7):1916.
CrossRef Google scholar
[16]
Mak KK, Wong YH, Pichika MR. Artificial intelligence in drug discovery and development. Drug Discov. Eval.: Saf. Pharmacokinet. Assays. 2023:1–38.
CrossRef Google scholar
[17]
Kwon S, Bae H, Jo J, Yoon S. Comprehensive ensemble in QSAR prediction for drug discovery. BMC Bioinf. 2019;20:1–12.
CrossRef Google scholar
[18]
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25:1315–1360.
CrossRef Google scholar
[19]
Keyvanpour MR, Shirzad MB. An analysis of QSAR research based on machine learning concepts. Curr Drug Discov Technol. 2021;18(1):17–30.
CrossRef Google scholar
[20]
Soares TA, Nunes-Alves A, Mazzolari A, Ruggiu F, Wei GW, Merz K. The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods. J Chem Inf Model. 2022;62(22):5317–5320.
CrossRef Google scholar
[21]
QSAR Toolbox [Online]. Available: https://qsartoolbox.org/. Accessed April 30, 2024.
[22]
Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov EN, Andrade CH. QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol. 2018;9:1275.
CrossRef Google scholar
[23]
Kumar A, Ojha PK, Roy K. QSAR modeling of chronic rat toxicity of diverse organic chemicals. Comput. Toxicol. 2023;26:100270.
CrossRef Google scholar
[24]
Suay-Garcia B, Bueso-Bordils JI, Falcó A, Pérez-Gracia MT, Antón-Fos G, Alemán-López P. Quantitative structure–activity relationship methods in the discovery and development of antibacterials. Wiley Interdiscip Rev Comput Mol Sci. 2020;10(6): e1472.
CrossRef Google scholar
[25]
Baudis S, Behl M. High-throughput and combinatorial approaches for the development of multifunctional polymers. Macromol Rapid Commun. 2022;43(12):2100400.
CrossRef Google scholar
[26]
Carell T, Wintner EA, Sutherland AJ, Rebek Jr J, Dunayevskiy YM, Vouros P. New promise in combinatorial chemistry: synthesis, characterization, and screening of small-molecule libraries in solution. Chem Biol. 1995;2(3):171–183.
CrossRef Google scholar
[27]
Kirschning A, Monenschein H, Wittenberg R. Functionalized polymers—emerging versatile tools for solution-phase chemistry and automated parallel synthesis. Angew Chem Int Ed. 2001;40(4):650–679.
CrossRef Google scholar
[28]
Kodadek T, Satz A. A history of selection-based high-throughput screening technologies for hit identification. In: Brunschweiger A, Young DW, eds. DNA-encoded Libraries. Cham: Springer;2022:. Topics in Medicinal Chemistry; vol. 40.
[29]
Shim MS, Kwon YJ. Efficient and targeted delivery of siRNA in vivo. FEBS J. 2010;277(23):4814–4827.
CrossRef Google scholar
[30]
Seyyednia E, Oroojalian F, Baradaran B, Mojarrad JS, Mokhtarzadeh A, Valizadeh H. Nanoparticles modified with vasculature-homing peptides for targeted cancer therapy and angiogenesis imaging. J Contr Release. 2021;338:367–393.
CrossRef Google scholar
[31]
Zhao Q, Zhu Z, Dimitrov DS. Yeast display of engineered antibody domains. Therapeut. Protein: Methods Protoc. 2012:73–84
CrossRef Google scholar
[32]
Fux AC, Casonato Melo C, Schlahsa L, et al. Generation of endotoxin-specific monoclonal antibodies by phage and yeast display for capturing endotoxin. Int J Mol Sci. 2024;25(4):2297.
CrossRef Google scholar
[33]
Lu RM, Wu CH, Patil AV, Wu HC. Peptide targeting methods. Handb. Vivo Chem. Mice: Lab. Living Syst. 2020:451–488
CrossRef Google scholar
[34]
Bolognesi ML, Calonghi N, Mangano C, Masotti L, Melchiorre C. Parallel synthesis and cytotoxicity evaluation of a polyamine– quinone conjugates library. J Med Chem. 2008;51(17):5463–5467.
CrossRef Google scholar
[35]
Upadhya R, Kosuri S, Tamasi M, et al. Automation and data-driven design of polymer therapeutics. Adv Drug Deliv Rev. 2021;171:1–28.
CrossRef Google scholar
[36]
Ahsan H, Ahmad R. Multiplex technology for biomarker immunoassays. Innate Immun. Health Dis. 2020:1–10
CrossRef Google scholar
[37]
Mende M, Tsouka A, Heidepriem J, et al. On-chip neo-glycopeptide synthesis for multivalent glycan presentation. Chem–Eur J. 2020;26(44):9954–9963.
CrossRef Google scholar
[38]
Duran-Frigola M, Pauls E, Guitart-Pla O, et al. Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol. 2020;38(9):1087–1096.
CrossRef Google scholar
[39]
Davila-Calderon J, Patwardhan NN, Chiu LY, et al. IRES-targeting small molecule inhibits enterovirus 71 replication via allosteric stabilization of a ternary complex. Nat Commun. 2020;11(1):4775.
CrossRef Google scholar
[40]
Naseri G, Koffas MA. Application of combinatorial optimization strategies in synthetic biology. Nat Commun. 2020;11(1):2446.
CrossRef Google scholar
[41]
Asmar AJ, Abrams SR, Hsin J, et al. A ubiquitin-based effector-to-inhibitor switch coordinates early brain, craniofacial, and skin development. Nat Commun. 2023;14(1):4499.
CrossRef Google scholar
[42]
Das B, Reddy NS, Rathod AK, Avula SK, Das R. Our contribution to microwave-assisted conversions of bioactive compounds. Curr. Microw. Chem. 2023;10(2):198–207.
CrossRef Google scholar
[43]
Sabljic A. QSAR models for estimating properties of persistent organic pollutants required in evaluation of their environmental fate and risk. Chemosphere. 2001;43(3):363–375.
CrossRef Google scholar
[44]
Profiling QSAR Toolbox [Online]. Available: https://qsartoolbox.org/features/profiling/. Accessed April 30, 2024.
[45]
The OECD QSAR Toolbox -oecd [Online]. Available: https://oecd.org/chemicalsafety/risk-assessment/oecd-qsar-toolbox.htm. Accessed April 30, 2024.
[46]
Liu R, Li X, Lam KS. Combinatorial chemistry in drug discovery. Curr Opin Chem Biol. 2017;38:117–126.
CrossRef Google scholar
[47]
Seneci P, Fassina G, Frecer V, Miertus S. The effects of combinatorial chemistry and technologies on drug discovery and biotechnology: a mini review. Nova biotechnologica et chimica. 2014;13(2):87–108.
CrossRef Google scholar
[48]
Kodadek T. The rise, fall and reinvention of combinatorial chemistry. Chem Commun. 2011;47(35):9757–9763.
CrossRef Google scholar
[49]
Combinatorial chemistry -an overview | ScienceDirect topics [Online]. Available: https://sciencedirect.com/topics/chemistry/combinatorial-chemistry. Accessed April 30, 2024.
[50]
Atienza JJ, Arcinue RJ, Butalid MD, Maristela MM, de Grano RV, Labrador A. In silico evaluation of the inhibitory property of Holothuria scabra (sea cucumber) with the catalytic domain of matrix metalloproteinase-1 for collagen degradation via interaction of triterpenoid saponins. J Pharmacogn Phytochem. 2022;11(2):247–257.
CrossRef Google scholar
[51]
Liu R, Li X, Lam KS. Combinatorial chemistry in drug discovery. Curr Opin Chem Biol. 2017;38:117–126.
CrossRef Google scholar
[52]
Basic concepts of microarrays and potential applications in clinical microbiology - pmc [Online]. Available: https://ncbi.nlm.nih.gov/pmc/articles/PMC2772365/. Accessed April 30, 2024.
[53]
An H, Cummins LL, Griffey RH, et al. Solution phase combinatorial chemistry. Discovery of novel polyazapyridinophanes with potent antibacterial activity by a solution phase simultaneous addition of functionalities approach. J Am Chem Soc. 1997;119(16):3696–3708.
CrossRef Google scholar
[54]
Zhang W, Luo Z, Chen CHT, Curran DP. Solution-phase preparation of a 560-compound library of individual pure mappicine analogues by fluorous mixture synthesis. J Am Chem Soc. 2002;124(35):10443–10450.
CrossRef Google scholar
[55]
DECL remarks & outlook -DNA encoded chemical library [Online]. Available: https://decltechnology.com/decl-remarks-outlook/. Accessed April 30, 2024.
[56]
Satz AL, Brunschweiger A, Flanagan ME, et al. DNA-encoded chemical libraries. Nat. Rev. Methods Prim. 2022;2(1):3.
CrossRef Google scholar
[57]
DNA-encoded chemical libraries - WuXi Biology [Online]. Available: https://wuxibiology.com/resource/dna-encoded-chemical-libraries-2/. Accessed April 30, 2024.
[58]
Liu T, Qian Z, Xiao Q, Pei D. High-throughput screening of one-bead-one-compound libraries: identification of cyclic peptidyl inhibitors against calcineurin/NFAT interaction. ACS Comb Sci. 2011;13(5):537–546.
CrossRef Google scholar
[59]
Lam KS, Lehman AL, Song A, et al. Synthesis and screening of “one-bead one-compound” combinatorial peptide libraries. Methods Enzymol. 2003;369:298–322. Academic Press.
CrossRef Google scholar
[60]
Newton MS, Cabezas-Perusse Y, Tong CL, Seelig B. In vitro selection of peptides and proteins—advantages of mRNA display. ACS Synth Biol. 2019;9(2):181–190.
CrossRef Google scholar
[61]
Wilson DS, Keefe AD, Szostak JW. The use of mRNA display to select high-affinity protein-binding peptides. Proc Natl Acad Sci USA. 2001;98(7):3750–3755.
CrossRef Google scholar
[62]
Alfaleh MA, Alsaab HO, Mahmoud AB, et al. Phage display derived monoclonal antibodies: from bench to bedside. Front Immunol. 2020;11:567223.
CrossRef Google scholar
[63]
Bazan J, Całkosiński I, Gamian A. Phage display—a powerful technique for immunotherapy: 1. Introduction and potential of therapeutic applications. Hum Vaccines Immunother. 2012;8(12):1817–1828.
CrossRef Google scholar
[64]
Fermin G, Rampersad S, Tennant P. Viruses as tools of biotechnology: therapeutic agents, carriers of therapeutic agents and genes, nanomaterials, and more. Viruses: Mol. Biol. Host Interact. Biotechnol. Appl. 2018:291–316 https://doi.org/10.1016/B978-0-12-811257-1.00012-7.
[65]
Mishra M, Sharma M, Dubey R, Kumari P, Ranjan V, Pandey J. Green synthesis interventions of pharmaceutical industries for sustainable development. Current. Res. Green Sustain. Chem. 2021;4:100174.
CrossRef Google scholar
[66]
Helwig K, Niemi L, Stenuick JY, et al. Broadening the perspective on reducing pharmaceutical residues in the environment. Environ Toxicol Chem. 2024;43(3):653–663.
CrossRef Google scholar
[67]
Basics of Green Chemistry | US EPA. Accessed: April. 30, 2024. [Online]. Available: https://epa.gov/greenchemistry/basics-green-chemistry. Accessed April 30, 2024.
[68]
Taber GP, Pfisterer DM, Colberg JC. A new and simplified process for preparing N-[4-(3, 4-dichlorophenyl)-3, 4-dihydro-1 (2H)-naphthalenylidene] methanamine and a telescoped process for the synthesis of (1 S-cis)-4-(3, 4-dichlorophenol)-1, 2, 3, 4-tetrahydro-N-methyl-1-naphthalenamine mandelate: key intermediates in the synthesis of sertraline hydrochloride. Org Process Res Dev. 2004;8(3):385–388.
CrossRef Google scholar
[69]
Ji J, Barnes DM, Zhang J, King SA, Wittenberger SJ, Morton HE. Catalytic enantioselective conjugate addition of 1, 3-dicarbonyl compounds to nitroalkenes. J Am Chem Soc. 1999;121(43):10215–10216.
CrossRef Google scholar
[70]
Barnes DM, Ji J, Fickes MG, et al. Development of a catalytic enantioselective conjugate addition of 1, 3-dicarbonyl compounds to nitroalkenes for the synthesis of endothelin-A antagonist ABT-546. Scope, mechanism, and further application to the synthesis of the antidepressant rolipram. J Am Chem Soc. 2002;124(44):13097–13105.
CrossRef Google scholar
[71]
Barnes DM, Ji J, Zhang J, King SA, Wittenberger SJ, Morton HE. Development of a Catalytic, Asymmetric Michael Addition in the Synthesis of Endothelin Antagonist ABT-546. 2002.
CrossRef Google scholar
[72]
Atorvastatin - an overview | ScienceDirect topics [Online]. Available: https://sciencedirect.com/topics/chemistry/atorvastatin. Accessed April 30, 2024.
[73]
How AstraZeneca is making drug discovery sustainable [Online]. Available: https://astrazeneca.com/what-science-can-do/topics/sustainability/Striving-for-sustainable-drug-discovery-using-Green-Chemistry.html. Accessed April 30, 2024.
[74]
Brown D. Future pathways for combinatorial chemistry. Mol Divers. 1997;2:217–222.
CrossRef Google scholar
[75]
Zhou Z, Xu T, Zhang X. Empowerment of AI algorithms in biochemical sensors. TrAC, Trends Anal Chem. 2024;117613.
CrossRef Google scholar
[76]
Ayres LB, Gomez FJ, Linton JR, Silva MF, Garcia CD. Taking the leap between analytical chemistry and artificial intelligence: a tutorial review. Anal Chim Acta. 2021;1161:338403.
CrossRef Google scholar
[77]
Presidential green chemistry challenge:2002 greener synthetic pathways award | US EPA [Online]. Available: https://epa.gov/greenchemistry/presidential-green-chemistry-challenge-2002-greener-synthetic-pathways-award. Accessed April 30, 2024.
[78]
Gormley AJ, Webb MA. Machine learning in combinatorial polymer chemistry. Nat Rev Mater. 2021;6(8):642–644.
CrossRef Google scholar
[79]
Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: a paradigm shift in big data analysis. Prog. Mol. Biol. Transl. Sci. 2022;190(1):57–100.
CrossRef Google scholar
[80]
Javaid M, Haleem A, Singh RP, Suman R. Artificial intelligence applications for industry 4.0: a literature-based study. J. Ind. Integrat. Manag. 2022;7(1):83–111.
CrossRef Google scholar
[81]
Florian E, Sgarbossa F, Zennaro I. Machine learning-based predictive maintenance: a cost-oriented model for implementation. Int J Prod Econ. 2021;236:108114.
CrossRef Google scholar
[82]
Petiwala FF, Shukla VK, Vyas S. IBM Watson: redefining artificial intelligence through cognitive computing. In: Proceedings of International Conference on Machine Intelligence and Data Science Applications: MIDAS 2020. Singapore: Springer;2021:173–185.
CrossRef Google scholar
[83]
Muratov EN, Bajorath J, Sheridan RP, et al. QSAR without borders. Chem Soc Rev. 2020;49(11):3525–3564.
CrossRef Google scholar
[84]
Tan P, Chen X, Zhang H, Wei Q, Luo K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin Cancer Biol. 2023, February;89:61–75. Academic Press.
CrossRef Google scholar
[85]
Ayon NJ. High-throughput screening of natural product and synthetic molecule libraries for antibacterial drug discovery. Metabolites. 2023;13(5):625.
CrossRef Google scholar
[86]
Chen TL, Kim H, Pan SY, Tseng PC, Lin YP, Chiang PC. Implementation of green chemistry principles in circular economy system towards sustainable development goals: challenges and perspectives. Sci Total Environ. 2020;716:136998.
CrossRef Google scholar
[87]
Harutyunyan LR, Lasareva EV. Chitosan and its derivatives: a step towards green chemistry. Biointerfaces Res. Appl. Chem. 2023;13:578. https://doi.org/10.33263/BRIAC136.578.
[88]
Gbadago DQ, Hwang G, Lee K, Hwang S. Deep learning for green chemistry: an AI-enabled pathway for biodegradability prediction and organic material discovery. SSRN 4689153 https://doi.org/10.2139/ssrn.4689153;2024.
[89]
Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital innovation enabled nanomaterial manufacturing; machine learning strategies and green perspectives. Nanomaterials. 2022;12(15):2646.
CrossRef Google scholar

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