Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

He, Sheng; Leanse, Leon G.; Feng, Yanfang

doi:10.1016/j.addr.2021.113922

Cited by 66 publications

(21 citation statements)

References 236 publications

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“…To perform quantitative investigation for interaction between media, carrier and drug or gene, a broad spectrum of precise computational strategies have been launched: docking, QSAR, quantum mechanics, 170 chemoinformatic modelling 171 coarse‐grained, 172 Monte Carlo 172 and molecular dynamics (MD) 172 . Some methods are deployed in an odd assortment of software packages in MD, encompassing LAMPPS, GROMACS, CHARMM, AMBER and NAMD 173 …”

Section: Computational Approaches For Gene Delivery: Room For Improve...mentioning

confidence: 99%

“…172 Some methods are deployed in an odd assortment of software packages in MD, encompassing LAMPPS, GROMACS, CHARMM, AMBER and NAMD. 173 Apart from those, other NP-based continuum models, such as the finite element method and the finite volume method, are used to determine the motion of drugs or genes via the vascular system. 174,175 These computational models could enhance drug and gene delivery efficacy as well as the therapeutic efficiency of bionanomedicine.…”

Section: Computational Approaches For Gene Delivery: Room For Improve...mentioning

confidence: 99%

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Histidine‐enhanced gene delivery systems: The state of the art

Hooshmand

Sabet

Hasanzadeh

et al. 2022

The Journal of Gene Medicine

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Gene therapy has emerged as a promising tool for treating different intractable diseases, particularly cancer or even viral diseases such as COVID-19 (coronavirus disease 2019). In this context, various non-viral gene carriers are being explored to transfer DNA or RNA sequences into target cells. Here, we review the applications of the naturally occurring amino acid histidine in the delivery of nucleic acids into cells.The biocompatibility of histidine-enhanced gene delivery systems has encouraged their wider use in gene therapy. Histidine-based gene carriers can involve the modification of peptides, dendrimers, lipids or nanocomposites. Several linear polymers, such as polyethylenimine, poly-L-lysine (synthetic) or dextran and chitosan (natural), have been conjugated with histidine residues to form complexes with nucleic acids for intracellular delivery. The challenges, opportunities and future research trends of histidine-based gene deliveries are investigated.

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Section: Computational Approaches For Gene Delivery: Room For Improve...mentioning

confidence: 99%

Section: Computational Approaches For Gene Delivery: Room For Improve...mentioning

confidence: 99%

Histidine‐enhanced gene delivery systems: The state of the art

Hooshmand

Sabet

Hasanzadeh

et al. 2022

The Journal of Gene Medicine

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“…A classification application of kNN was reported by Karakoc et al for classification of small molecules based on selecting the most relevant set of chemical descriptors used for ultimate discrimination between active and inactive compounds on various biological systems ( Karakoc et al, 2007 ). A comprehensive list of kNN applications in classification and regression tasks all applied toward drug delivery for infectious disease treatment, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction was reported by He et al (2021) .…”

Section: Modern Approachesmentioning

confidence: 99%

“…It should be noted that modern software implementations of machine learning algorithms efficiently utilize computer hardware and are ideal for the bioinformatics or chemoinformatics scenario; however, extreme care should be taken with input data ( Bzdok et al, 2017 ). Most importantly, the increasing availability of data makes machine learning methods even more important, either as a stand-alone method or in a consensus scenario where they can boost traditional medicinal chemistry approaches ( He et al, 2021 ). In this review, we focus on machine learning approaches in CADD that have been reported in recent years and have been used in the development of novel antibacterials.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Antibacterial Drug Design

Jukič

Bren

2022

Front. Pharmacol.

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Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

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“…In order to achieve optimal antimicrobial dosing, several approaches are implemented in the clinical practice settings or under the experimental phase to provide individualized dosing and address some degree of variabilities. These include dosing nomograms, therapeutic drug monitoring (TDM), dosing software and other novel technology-based approaches, such as real-time drug monitoring using biosensors, closed-loop controlled systems, artificial intelligence and machine learning assisted systems ( He et al, 2021 ; Rawson et al, 2021 ). These technology-based approaches have several barriers to applying in clinical practice including inadequate investigation on their applications, the issue of data integration with patient medical records ( Rawson et al, 2021 ).…”

Section: Pk/pd Considerations When Optimizing Antibiotic Dosingmentioning

confidence: 99%

Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing

Yow

Govindaraju

Lim³

et al. 2022

Front. Pharmacol.

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In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.

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Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

Cited by 66 publications

References 236 publications

Histidine‐enhanced gene delivery systems: The state of the art

Histidine‐enhanced gene delivery systems: The state of the art

Machine Learning in Antibacterial Drug Design

Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing

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