When Cyclodextrins Met Data Science: Unveiling Their Pharmaceutical Applications through Network Science and Text-Mining

A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins

Cyclodextrins are macrocyclic rings composed of glucose residues. Due to their remarkable structural properties, they can form host-guest inclusion complexes, which is why they are frequently used in the pharmaceutical, cosmetic, and food industries, as well as in environmental and analytical chemistry. This review presents the reports from 2011 to 2023 on the quantitative structure-activity/property relationship (QSAR/QSPR) approach, which is primarily employed to predict the thermodynamic stability of inclusion complexes. This article extensively discusses the significant developments related to the size of available experimental data, the available sets of descriptors, and the machine learning (ML) algorithms used, such as support vector machines, random forests, artificial neural networks, and gradient boosting. As QSAR/QPR analysis only requires molecular structures of guests and experimental values of stability constants, this approach may be particularly useful for predicting these values for complexes with randomly substituted cyclodextrins, as well as for estimating their dependence on pH. This work proposes solutions on how to effectively use this knowledge, which is especially important for researchers who will deal with this topic in the future. This review also presents other applications of ML in relation to CD complexes, including the prediction of physicochemical properties of CD complexes, the development of analytical methods based on complexation with CDs, and the optimisation of experimental conditions for the preparation of the complexes.

Change in the Kinetic Regime of Aggregation of Yeast Alcohol Dehydrogenase in the Presence of 2-Hydroxypropyl-β-cyclodextrin

Chemical chaperones are low-molecular-weight compounds that suppress protein aggregation. They can influence different stages of the aggregation process-the stage of protein denaturation, the nucleation stage and the stage of aggregate growth-and this may lead to a change in the aggregation kinetic regime. Here, the possibility of changing the kinetic regime in the presence of a chemical chaperone 2-hydroxypropyl-β-cyclodextrin (2-HP-β-CD) was investigated for a test system based on the thermally induced aggregation of yeast alcohol dehydrogenase (yADH) at 56 °C. According to differential scanning calorimetry data, 2-HP-β-CD did not affect the stage of the protein molecule unfolding. Dynamic light scattering data indicated changes in the aggregation kinetics of yADH during the nucleation and aggregate growth stages in the presence of the chaperone. The analysis of kinetic curves showed that the order of aggregation with respect to protein (nc), calculated for the stage of aggregate growth, changed from nc = 1 to nc = 2 with the addition of 100 mM 2-HP-β-CD. The mechanism of 2-HP-β-CD action on the yADH thermal aggregation leading to a change in its kinetic regime of aggregation is discussed.

The Role of Cyclodextrin in the Construction of Nanoplatforms: From Structure, Function and Application Perspectives

Cyclodextrins (CyDs) in nano drug delivery systems have received much attention in pursuit of good compatibility, negligible toxicity, and improved pharmacokinetics of drugs. Their unique internal cavity has widened the application of CyDs in drug delivery based on its advantages. Besides this, the polyhydroxy structure has further extended the functions of CyDs by inter- and intramolecular interactions and chemical modification. Furthermore, the versatile functions of the complex contribute to alteration of the physicochemical characteristics of the drugs, significant therapeutic promise, a stimulus-responsive switch, a self-assembly capability, and fiber formation. This review attempts to list recent interesting strategies regarding CyDs and discusses their roles in nanoplatforms, and may act as a guideline for developing novel nanoplatforms. Future perspectives on the construction of CyD-based nanoplatforms are also discussed at the end of this review, which may provide possible direction for the construction of more rational and cost-effective delivery vehicles.

Antibacterial drugs and cyclodextrin inclusion complexes: a patent review

INTRODUCTION

Bacterial antibiotic resistance occurs when bacteria mutate and escape the effect of antibiotics, which makes the antibiotics no longer effective in treating infections. New solutions for bacterial infections are a persistent need including the identification of drugs with better pharmacological profiles, more potent, and safer. Cyclodextrins inclusion complexes have been able to improve the physicochemical and pharmacological properties of the formulation molecules, resulting in new alternatives with better efficacy.

AREAS COVERED

The patents analyzed in the review used treatments based on antibiotics already on the market, natural products, and synthesized molecules composed of the formulation with cyclodextrins. The combination between cyclodextrin and nanostructures also were presented in the patents review process. Moreover, inclusion complexes have been an alternative in developing treatment mainly in China by the pharmaceutical industries in several countries such as Germany, Hungary, the United States of America, Japan and China.

EXPERT OPINION

This review is broad and complete since it considers the first patent involving cyclodextrins and antibacterial drugs. Therefore, the various inclusion complexes and antibacterial drugs alternatives presented in this review offer therapeutic options to fight bacterial infections. If shown to be effective, these drugs may be extremely important in the current clinical practice.

Cyclodextrin Inclusion Complexes with Antibiotics and Antibacterial Agents as Drug-Delivery Systems—A Pharmaceutical Perspective

Cyclodextrins (CDs) are a family of cyclic oligosaccharides, consisting of a macrocyclic ring of glucose subunits linked by α-1,4 glycosidic bonds. The shape of CD molecules is similar to a truncated cone with a hydrophobic inner cavity and a hydrophilic surface, which allows the formation of inclusion complexes with various molecules. This review article summarises over 200 reports published by the end of 2021 that discuss the complexation of CDs with antibiotics and antibacterial agents, including beta-lactams, tetracyclines, quinolones, macrolides, aminoglycosides, glycopeptides, polypeptides, nitroimidazoles, and oxazolidinones. The review focuses on drug-delivery applications such as improving solubility, modifying the drug-release profile, slowing down the degradation of the drug, improving biological membrane permeability, and enhancing antimicrobial activity. In addition to simple drug/CD combinations, ternary systems with additional auxiliary substances have been described, as well as more sophisticated drug-delivery systems including nanosponges, nanofibres, nanoparticles, microparticles, liposomes, hydrogels, and macromolecules. Depending on the desired properties of the drug product, an accelerated or prolonged dissolution profile can be achieved when combining CD with antibiotics or antimicrobial agents.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Exploring In Vitro Biological Cellular Responses of Pegylated β-Cyclodextrins

βCDPEG5 and βCDPEG2 are two derivatives comprising seven PEG linear chains of 5 and 2 kDa, respectively, conjugated to βCD. As βCDPEGs display different physicochemical properties than their precursors, they could also trigger distinct cellular responses. To investigate the biological behavior of βCDPEGs in comparison to their parent compounds, we performed broad toxicological assays on RAW 264.7 macrophages, MC3T3-E1 osteoblasts, and MDCK cells. By analyzing ROS and NO₂⁻ overproduction in macrophages, we found that βCDPEGs induced a moderate stress response without affecting cell viability. Although MC3T3-E1 osteoblasts were more sensitive than MDCK cells to βCDPEGs and the parent compounds, a similar pattern was observed: the effect of βCDPEG5 on cell viability and cell cycle progression was larger than that of βCDPEG2; PEG2 affected cell viability and cell cycle more than βCDPEG2; cell post-treatment recovery was favorable in all cases, and the compounds had similar behaviors regarding ROS generation. The effect on MDCK cell migration followed a similar pattern. In contrast, for osteoblasts, the interference of βCDPEG5 with cell migration was smaller than that of βCDPEG2; likewise, the effect of PEG2 was shorter than its conjugate. Overall, the covalent conjugation of βCD and PEGs, particularly to yield βCDPEG2, improved the biocompatibility profile, evidencing that a favorable biological response can be tuned through a thoughtful combination of materials. Moreover, this is the first time that an in vitro evaluation of βCD and PEG has been presented for MC3T3-E1 and MDCK cells, thus providing valuable knowledge for designing biocompatible nanomaterials constructed from βCD and PEGs.

Activity of vehicles in the bus rapid transit system Metrobús in Mexico City

In this paper, we analyze a massive dataset with registers of the movement of vehicles in the bus rapid transit system Metrobús in Mexico City from February 2020 to April 2021. With these records and a division of the system into 214 geographical regions (segments), we characterize the vehicles' activity through the statistical analysis of speeds in each zone. We use the Kullback-Leibler distance to compare the movement of vehicles in each segment and its evolution. The results for the dynamics in different zones are represented as a network where nodes define segments of the system Metrobús and edges describe similarity in the activity of vehicles. Community detection algorithms in this network allow the identification of patterns considering different levels of similarity in the distribution of speeds providing a framework for unsupervised classification of the movement of vehicles. The methods developed in this research are general and can be implemented to describe the activity of different transportation systems with detailed records of the movement of users or vehicles.

Application of text-mining techniques for extraction and analysis of paracetamol and ibuprofen marketed products' qualitative composition

Text mining (TM) applications in the field of biomedicine are gaining great interest. TM tools can facilitate formulation development by analyzing textual information from patent databases, scientific articles, summary of products characteristics, etc. The aim of this study was to utilize TM tools to perform qualitative analysis of paracetamol (PAR) and ibuprofen (IBU) formulations, in terms of identifying and evaluating the presence of excipients specific to the active pharmaceutical ingredient (API) and/or dosage form. A total of 152 products were analyzed. Web-scraping was used to retrieve the data, and Python-based open-source software Orange 3.31.1 was used for TM and statistical analysis (ANOVA) of the obtained results. The majority of marketed products for both APIs were tablets. The predominant excipients in all tablet formulations were povidone, starch, microcrystalline cellulose and hypromellose. Povidone, stearic acid, potassium sorbate, maize starch and pregelatinized starch occurred more frequently in PAR tablets. On the other hand, titanium dioxide, lactose, shellac, sucrose and ammonium hydroxide were specific to IBU tablets. PAR oral suspensions more frequently contained dispersible cellulose; liquid sorbitol; methyl and propyl parahydroxybenzoate, glycerol and acesulfame potassium. Specific excipients in other PAR dosage forms, such as effervescent tablets, hard capsules, oral powders, solutions and suspensions, as well as IBU gels and soft capsules, were also evaluated.