Artificial Neural Networks in Pharmaceutical Research, Drug Delivery and Pharmacy Curriculum

Sutariya, Vijaykumar; Groshev, Anastasia; Pathak, Yashwant

doi:10.1109/sbec.2013.54

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is noteworthy that most of the applied algorithms in the studies belong to the field of supervised learning. Algorithms such as support vector machines [93,94], random forests [95,96], and neural networks [97,98] are applied to already known drug discovery problems in order to identify the most important influencing variables based on the given input data. However, the limitations of supervised learning are that it can only be applied to classification problems that are already well known.…”

Section: Future Research Agendamentioning

confidence: 99%

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Karger

Kureljusic

2022

Pharmaceuticals

View full text Add to dashboard Cite

Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.

show abstract

Section: Future Research Agendamentioning

confidence: 99%

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Karger

Kureljusic

2022

Pharmaceuticals

View full text Add to dashboard Cite

show abstract

“…Since then, digital modeling is emancipating the state-of-the-art approach for delivering process and product lifecycle awareness, enabling unparalleled plantwide control, optimization, and prediction for material manufacturing [ 3 ]. Akin to such product material ontologies, artificial neural networks (ANNs) have ascended as a surrogate, responsive framework, poised to identify and simulate non-linear dependencies between digital twin variables [ 4 ]. As such, ANNs constitute the requirements for demanding, multiple, complex experiments towards the generation of the product manufacturing cycle (PML) data, irrelevant.…”

Section: Introductionmentioning

confidence: 99%

Pharma 4.0-Artificially Intelligent Digital Twins for Solidified Nanosuspensions

Davidopoulou

2022

Pharmaceutics

View full text Add to dashboard Cite

Digital twins capacitate the industry 4.0 paradigm by predicting and optimizing the performance of physical assets of interest, mirroring a realistic in-silico representation of their functional behaviour. Although advanced digital twins set forth disrupting opportunities by delineating the in-service product and the related process dynamic performance, they have yet to be adopted by the pharma sector. The latter, currently struggles more than ever before to improve solubility of BCS II i.e., hard-to-dissolve active pharmaceutical ingredients by micronization and subsequent stabilization. Herein we construct and functionally validate the first artificially intelligent digital twin thread, capable of describing the course of manufacturing of such solidified nanosuspensions given a defined lifecycle starting point and predict and optimize the relevant process outcomes. To this end, we referenced experimental data as the sampling source, which we then augmented via pattern recognition utilizing neural network propagations. The zeta-dynamic potential metrics of the nanosuspensions were correlated to the interfacial Gibbs energy, while the density and heat capacity of the material system was calculated via the Saft-γ-Mie statistical fluid theory. The curated data was then fused to physical and empirical laws to choose the appropriate theory and numeric description, respectively, before being polished by tuning the critical parameters to achieve the best fit with reality.

show abstract

“…The concept of ANNs has been used in different fields of pharmaceutical research since the 1990s 23,24. It has been used to model the methods used to analyze some drugs: eg, ranitidine and corynoxeine 25–27.…”

Section: Introductionmentioning

confidence: 99%

Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks

Shalaby

Soliman

Casettari

et al. 2014

IJN

View full text Add to dashboard Cite

In this study, di- and triblock copolymers based on polyethylene glycol and polylactide were synthesized by ring-opening polymerization and characterized by proton nuclear magnetic resonance and gel permeation chromatography. Nanoparticles containing noscapine were prepared from these biodegradable and biocompatible copolymers using the nanoprecipitation method. The prepared nanoparticles were characterized for size and drug entrapment efficiency, and their morphology and size were checked by transmission electron microscopy imaging. Artificial neural networks were constructed and tested for their ability to predict particle size and entrapment efficiency of noscapine within the formed nanoparticles using different factors utilized in the preparation step, namely polymer molecular weight, ratio of polymer to drug, and number of blocks that make up the polymer. Using these networks, it was found that the polymer molecular weight has the greatest effect on particle size. On the other hand, polymer to drug ratio was found to be the most influential factor on drug entrapment efficiency. This study demonstrated the ability of artificial neural networks to predict not only the particle size of the formed nanoparticles but also the drug entrapment efficiency. This may have a great impact on the design of polyethylene glycol and polylactide-based copolymers, and can be used to customize the required target formulations.

show abstract

Artificial Neural Networks in Pharmaceutical Research, Drug Delivery and Pharmacy Curriculum

Cited by 4 publications

References 9 publications

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Pharma 4.0-Artificially Intelligent Digital Twins for Solidified Nanosuspensions

Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks

Contact Info

Product

Resources

About