Computational pharmaceutics - A new paradigm of drug delivery

Wang, Wei; Ye, Zhuyifan; Gao, Hanlu; Ouyang, Defang

doi:10.1016/j.jconrel.2021.08.030

Cited by 113 publications

(58 citation statements)

References 170 publications

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“…Furthermore, it is reasonable, time demanding, and effective more than any conventional technology. The computer-based drug designing method predicts the function of any individual protein of interest that conducts a very comprehensive approach [55]. It has various applications among them protein-ligand docking is one of them.…”

Section: Molecular Dockingmentioning

confidence: 99%

Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging and Re-Emerging Infectious Diseases

Aljahdali¹,

Molla²,

Ahammad³

2022

Antiviral Drugs - Intervention Strategies

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Infectious diseases are initiated by small pathogenic living germs that are transferred from person to person by direct or indirect contact. Recently, different newly emerging and reemerging infectious viral diseases have become greater threats to human health and global stability. Investigators can anticipate epidemics through the advent of numerous mathematical tools that can predict specific pathogens and identify potential targets for vaccine and drug design and will help to fight against these challenges. Currently, computational approaches that include mathematical and essential tools have unfolded the way for a better understanding of newly originated emerging and re-emerging infectious disease, pathogenesis, diagnosis, and treatment option of specific diseases more easily, where immunoinformatics plays a crucial role in the discovery of novel peptides and vaccine candidates against the different viruses within a short time. Computational approaches include immunoinformatics, and computer-aided drug design (CADD)-based model trained biomolecules that offered reasonable and quick implementation approaches for the modern discovery of effective viral therapies. The essence of this review is to give insight into the multiple approaches not only for the detection of infectious diseases but also profound how people can pick appropriate models for the detection of viral therapeutics through computational approaches.

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Section: Molecular Dockingmentioning

confidence: 99%

Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging and Re-Emerging Infectious Diseases

Aljahdali¹,

Molla²,

Ahammad³

2022

Antiviral Drugs - Intervention Strategies

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“…In the production of nanomedicines, a systematic investigation of the process variables with impact on the quality features of the drug product contributes to improved predictability and reduces the risk of quality-related failure (7,34,35). In this context, pharmaceutical development widely follows the principles of QbD.…”

Section: Applications In Formulation and Productionmentioning

confidence: 99%

“…Many studies can be found in the literature using DoE techniques for optimization of nanoformulations (44)(45)(46)(47)(48)(49)(50). The accuracy of these predictions as well as the safety levels achieved with superior process controls have attracted the attention of Big Tech companies as well as regulatory agencies (7).…”

Section: Applications In Formulation and Productionmentioning

confidence: 99%

“…The rise of systems thinking and quality-bydesign (QbD) strategies in production led to the evolution of "computational pharmaceutics." Multi-scale modeling techniques and artificial intelligence (AI) together change the face of pharmaceutical sciences by creating virtual process environments and reducing the number of experiments in process optimization (6,7). By compiling process parameters and quality attributes of drug products in real-time (Figure 1), excipient compositions and manufacturing processes can be optimized (7).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Nova

Lin

Shanehsazzadeh

et al. 2022

Front. Digit. Health

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Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

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“…Moreover, it is desirable to minimise the use of animals, and sourcing either human faecal or intestinal fluid samples can be difficult and expensive. For this reason, in silico methods of predicting drugs' microbiome depletion hold significant potential [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

et al. 2021

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Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug–microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs’ susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug–microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.

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Computational pharmaceutics - A new paradigm of drug delivery

Cited by 113 publications

References 170 publications

Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging and Re-Emerging Infectious Diseases

Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging and Re-Emerging Infectious Diseases

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

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