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Applied Sciences
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Applications of Artificial Intelligence in Pharmaceutics
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Applications of Artificial Intelligence in Pharmaceutics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 January 2022) | Viewed by 13602

Special Issue Editor

Prof. Dr. Aleksander Mendyk

E-Mail Website
Guest Editor
Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College,30-688 Kraków, Poland
Interests: machine learning; artificial intelligence; pharmaceutical technology; biopharmaceutics; clinical trials; statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) has transformed the modern world of artificial intelligence (AI), allowing us to deal with the gradually increasing complexity in our analyses. Systems such as artificial neural networks, decision trees, fuzzy logic or evolutionary computations are omnipresent in various areas of science and technology. Not surprisingly, AI/ML tools are also becoming more popular in the pharmaceutical sciences. The range of applications is vast and constantly growing, thus it is futile to enumerate them all in this short introduction. However, it is noteworthy that industrial applications are exploding, especially in the area of big data. A very recent spectacular success was the development of a new antibiotic, halicin, by MIT using deep learning artificial neural networks. The discovery phase is not the only focus here, as the manufacturing process and its optimization and control are also natural applications for AI/ML. This is an especially hot topic in the context of process analytical technologies (PAT) and quality-by-design (QbD) systems that are today obligatory in the pharmaceutical industry. AI/ML is supposed to transform pharmaceutical manufacturing towards Industry 4.0. In 2018, the FDA announced the Model-Informed Drug Development (MIDD) pilot program, which emphasizes the importance of advanced modeling approaches to “improve clinical trial efficiency, increase the probability of regulatory success, and optimize drug dosing/therapeutic individualization in the absence of dedicated trials”. It has been recently noticed that “advanced modeling” in MIDD is a very feasible area for AI/ML applications. Last but not least, AI/ML is gradually becoming a partner in research activities, where knowledge processed by these tools is generalized and new visions of a problem are presented. Thus, new hypotheses are conceived and validated based on the analysis of the AI/ML behavior. This includes data-mining and the induction of logical rules or identification of crucial variables (features) by data-driven ML approaches.

In light of the above, we call for publications covering every angle of AI/ML application in pharmaceutical sciences. From an industrial perspective, this could cover various phases of the product life-cycle: from early discovery, through preclinical and clinical development, to the manufacturing and postmarketing phase. On the pure scientific ground, there might be pharmaceutical applications involving predictive modeling (regression, classification), exploratory analyses and data-mining, pattern recognition and any other possible data science approaches to pharmaceutical science, such as natural language processing (NLP) for automated data harvesting from pharmaceutical literature. We hope to attract a variety of visions to conceive a fruitful scientific discussion of what can be done with AI/ML in the field of pharmaceutical sciences, along with how these applications can be accomplished. I look forward to your submissions and large body of exciting publications!

Dr. Aleksander Mendyk
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pharmaceutical sciences
  • process analytical technologies
  • quality by design
  • model-informed drug development
  • artificial intelligence
  • machine learning
  • data science
  • artificial neural networks
  • predictive modeling
  • data-mining
  • pharmaceutical manufacturing
  • drug design
  • drug development
  • Industry 4.0

Published Papers (5 papers)

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Research

15 pages, 3400 KiB  
Article
Machine Learning for the Identification of Hydration Mechanisms of Pharmaceutical-Grade Cellulose Polymers and Their Mixtures with Model Drugs
by Przemysław Talik and Aleksander Mendyk
Appl. Sci. 2021, 11(16), 7751; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167751 - 23 Aug 2021
Cited by 2 | Viewed by 1938
Abstract
Differently bound water molecules confined in hydrated hydroxypropyl cellulose (HPC) type MF and their mixtures (1:1 w/w) with lowly soluble salicylic acid and highly soluble sodium salicylate were investigated by differential scanning calorimetry (DSC). The obtained ice-melting DSC curves of [...] Read more.
Differently bound water molecules confined in hydrated hydroxypropyl cellulose (HPC) type MF and their mixtures (1:1 w/w) with lowly soluble salicylic acid and highly soluble sodium salicylate were investigated by differential scanning calorimetry (DSC). The obtained ice-melting DSC curves of the HPC/H2O samples were deconvoluted into multiple components, using a specially developed curve decomposition tool. The ice-melting enthalpies of the individual deconvoluted components were used to estimate the amounts of water in three states in the HPC matrix: free water (FW), freezing bound water (FBW), and non-freezing water (NFW). A search for an optimal number of Gaussian functions was carried out among all available samples of data and was based on the analysis of the minimum fitting error vs. the number of Gaussians. Finally, three Gaussians accounting for three fractions of water were chosen for further analysis. The results of the calculations are discussed in detail and compared to previously obtained experimental DSC data. AI/ML tools assisted in theory elaboration and indirect validation of the hypothetical mechanism of the interaction of water with the HPC polymer. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Pharmaceutics)
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11 pages, 3175 KiB  
Article
Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics
by Zhengru Shen and Marco Spruit
Appl. Sci. 2021, 11(6), 2663; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062663 - 17 Mar 2021
Cited by 3 | Viewed by 2093
Abstract
The summary of product characteristics from the European Medicines Agency is a reference document on medicines in the EU. It contains textual information for clinical experts on how to safely use medicines, including adverse drug reactions. Using natural language processing (NLP) techniques to [...] Read more.
The summary of product characteristics from the European Medicines Agency is a reference document on medicines in the EU. It contains textual information for clinical experts on how to safely use medicines, including adverse drug reactions. Using natural language processing (NLP) techniques to automatically extract adverse drug reactions from such unstructured textual information helps clinical experts to effectively and efficiently use them in daily practices. Such techniques have been developed for Structured Product Labels from the Food and Drug Administration (FDA), but there is no research focusing on extracting from the Summary of Product Characteristics. In this work, we built a natural language processing pipeline that automatically scrapes the summary of product characteristics online and then extracts adverse drug reactions from them. Besides, we have made the method and its output publicly available so that it can be reused and further evaluated in clinical practices. In total, we extracted 32,797 common adverse drug reactions for 647 common medicines scraped from the Electronic Medicines Compendium. A manual review of 37 commonly used medicines has indicated a good performance, with a recall and precision of 0.99 and 0.934, respectively. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Pharmaceutics)
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10 pages, 1721 KiB  
Article
Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19
by Michael A. Kiebish, Punit Shah, Rangaprasad Sarangarajan, Vivek K. Vishnudas, Stephane Gesta, Poornima K. Tekumalla, Chas Bountra, Elder Granger, Eric Schadt, Leonardo O. Rodrigues and Niven R. Narain
Appl. Sci. 2021, 11(6), 2466; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062466 - 10 Mar 2021
Cited by 2 | Viewed by 2064
Abstract
The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public [...] Read more.
The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public health crisis with national security implications for many countries. The current study employed Bayesian networks to a longitudinal proteomic dataset generated from Caco-2 cells transfected with SARS-CoV-2 (isolated from patients returning from Wuhan to Frankfurt). Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis. Topology analysis identified a set of proteins directly linked to the SAR-CoV2 titer, including ACE2, a SARS-CoV-2 binding receptor, MAOB and CHECK1. Aligning with the topology analysis, MAOB and CHECK1 were also identified within the enriched drug-signatures. Taken together, the data output from this network has identified nodal host proteins that may be connected to 18 chemical compounds, some already marketed, which provides an immediate opportunity to rapidly triage these assets for safety and efficacy against COVID-19. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Pharmaceutics)
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12 pages, 344 KiB  
Article
Cocrystal Prediction Using Machine Learning Models and Descriptors
by Medard Edmund Mswahili, Min-Jeong Lee, Gati Lother Martin, Junghyun Kim, Paul Kim, Guang J. Choi and Young-Seob Jeong
Appl. Sci. 2021, 11(3), 1323; https://0-doi-org.brum.beds.ac.uk/10.3390/app11031323 - 01 Feb 2021
Cited by 21 | Viewed by 4551
Abstract
Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including [...] Read more.
Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Pharmaceutics)
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14 pages, 3522 KiB  
Article
Evolutionary Algorithms in Modeling Aerodynamic Properties of Spray-Dried Microparticulate Systems
by Adam Pacławski, Jakub Szlęk, Renata Jachowicz, Stefano Giovagnoli, Barbara Wiśniowska, Sebastian Polak, Natalia Czub and Aleksander Mendyk
Appl. Sci. 2020, 10(20), 7109; https://0-doi-org.brum.beds.ac.uk/10.3390/app10207109 - 13 Oct 2020
Viewed by 1822
Abstract
Spray drying is a single step process in which solutions or dispersions are converted into dry particles. It is widely used in pharmaceutical technology to produce inhalable particles. Dry particle behavior during inhalation, described as the emitted dose (ED) and fine particle fraction [...] Read more.
Spray drying is a single step process in which solutions or dispersions are converted into dry particles. It is widely used in pharmaceutical technology to produce inhalable particles. Dry particle behavior during inhalation, described as the emitted dose (ED) and fine particle fraction (FPF), is determined in vitro by standardized procedures. A large number of factors influencing the spray drying process and particle interaction makes it difficult to predict the final product properties in advance. This work presents the development of predictive models based on experimental data obtained by aerodynamic assessment of respirable dry powders. Developed models were tested according to the 10-fold cross-validation procedure and yielded good predictive ability. Both models were characterized by normalized root-mean-square error (NRMSE) below 8.50% and coefficient of determination (R2) above 0.90. Moreover, models were analyzed to establish a relationship between spray drying process parameters and the final product quality measures. Presented work describes the strategy of implementing the evolutionary algorithms in empirical model’s development. Obtained models can be applied as an expert system during pharmaceutical formulation development. The models have the potential for product optimization and a knowledge extraction to improve final quality of the drug. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Pharmaceutics)
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