Novel vanadyl complexes synthesis, characterization and interactions with bovine serum albumin-effects on STZ- diabetes rats

Drug-protein interactions are essential since most administered drugs bind abundantly and reversibly to serum albumin and are delivered mainly as a complex with protein. The nature and strength of drug-protein interactions have a big impact on how a drug works biologically. The binding parameters are useful in studying the pharmacological response of drugs and the designing of dosage forms. Serum albumin is regarded as optimal model for in vitro research on drug-protein interaction since it is the main protein that binds medicines and other physiological components. In this perspective, binary complex have been synthesized and characterized, from vanadium metal and acetylacetone(4,4,4-trifluoro-1-(2-theonyl)-1,3-butanedione). Imidazole, 2-Methyl-imidazole, and 2-Ethyl-imidazole auxiliary ligands were employed for the synthesis of ternary complexes. Additionally, UV absorption and fluorescence emission spectroscopy were used to examine the binding interactions between vanadium complexes and Bovine Serum Albumin. The outcomes of the binding studies and spectral approaches were in strong agreement with one another. These complexes upon inoculation into diabetes-induced Wistar rats stabilized their serum glucose levels within 3 days. From various studies, it was discovered that the ordering of glucose-lowering actions of these metal complexes were equivalent. The vanadium ternary metal complex derived from (4,4,4-trifluoro-1-(2-theonyl)-1,3-butanedione) and imidazole as ligands is the best among the other metal vanadium complexes.

Unlocking the potential of beta sitosterol: Augmenting the suppression of oral cancer cells through extrinsic and intrinsic signalling mechanisms

The global increase in the prevalence of oral neoplasms and related deaths can be attributed to social development and lifestyle factors, leading to poor prognosis and a lack of early clinical detection. Oral cancer ranks ranked sixth mostly diagnosed cancer and is a leading cause of cancer-related deaths. In light of these circumstances, our objective was to assess the potential of β-sitosterol, a naturally occurring herbal compound, as an anticancer agent against KB cells, a representative cell line for oral cancer. Our study primarily focused on evaluating the cytotoxic effect and mRNA expression of apoptotic proteins by β-sitosterol on KB cells. The results demonstrated a remarkable cytotoxic effect, leading to cell death. Further investigation using flow cytometric analysis revealed that this cell death was mediated through the initiation of the apoptotic signalling by β-sitosterol. The use of the bioinformatic tool, STITCH, supported our study by predicting drug-protein interactions and suggesting that β-sitosterol may play a significant role in targeting apoptotic pathways. Additionally, docking results were employed to validate the findings demonstrating high binding affinity of β-sitosterol with apoptotic-mediated signalling targets. To gain deeper insights into the molecular insights, we measured mRNA levels for BAX, BCL-2, MCL-1, P53, P21, MDM2, caspase3, and caspase9. Based on our comprehensive findings, our study concludes that β-sitosterol holds significant therapeutic potential against oral cancer cells. These results strongly suggest that this herbal compound should be further explored as a potential treatment option for oral cancer for clinical trial.

Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction

Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.

Recent advances in magnetic digital microfluidic platforms

Magnetic Digital microfluidics (DMF), which enables the manipulation of droplets containing different types of samples and reagents by permanent magnets or electromagnet arrays, has been used as a promising platform technology for bioanalytical and preparative assays. This is due to its unique advantages such as simple and "power free" operation, easy assembly, great compatibility with auto control systems, and dual functionality of magnetic particles (actuation and target attachment). Over the past decades, magnetic DMF technique has gained a widespread attention in many fields such as sample-to-answer molecular diagnostics, immunoassays, cell assays, on-demand chemical synthesis, and single-cell manipulation. In the first part of this review, we summarised features of magnetic DMF. Then, we introduced the actuation mechanisms and fabrication of magnetic DMF. Furthermore, we discussed five main applications of magnetic DMF, namely drug screening, protein assays, polymerase chain reaction (PCR), cell manipulation, and chemical analysis and synthesis. In the last part of the review, current challenges and limitations with magnetic DMF technique were discussed, such as biocompatibility, automation of microdroplet control systems, and microdroplet evaporation, with an eye on towards future development.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

NMR supersequences with real-time homonuclear broadband decoupling: Sequential acquisition of protein and small molecule spectra in a single experiment

NOAH (NMR byOrderedAcquisition using ¹H-detection) type of pure shift NMR pulse scheme has been designed for the efficient utilization of magnetization that presents in a spin-system under consideration. The proposed strategy, PROSMASH-HSQC² (PROtein-HSQC and SMAll molecule-HSQC Signals with Homodecoupling) uses the real-time BIRD pure shift NMR strategy and two HSQC spectra (¹³C-HSQC for small molecules and ¹⁵N-HSQC for ¹⁵N-isotopic labelled proteins) can be recorded in a single NMR experiment. Thus, this method permits precise determination of drug-protein interactions at atomic levels by monitoring the chemical shift perturbations, and will have potential applications in drug discovery programs.

Characterization of solution-phase drug-protein interactions by ultrafast affinity extraction

A number of tools based on high-performance affinity separations have been developed for studying drug-protein interactions. An example of one recent approach is ultrafast affinity extraction. This method has been employed to examine the free (or non-bound) fractions of drugs and other solutes in simple or complex samples that contain soluble binding agents. These free fractions have also been used to determine the binding constants and rate constants for the interactions of drugs with these soluble agents. This report describes the general principles of ultrafast affinity extraction and the experimental conditions under which it can be used to characterize such interactions. This method will be illustrated by utilizing data that have been obtained when using this approach to measure the binding and dissociation of various drugs with the serum transport proteins human serum albumin and alpha₁-acid glycoprotein. A number of practical factors will be discussed that should be considered in the design and optimization of this approach for use with single-column or multi-column systems. Techniques will also be described for analyzing the resulting data for the determination of free fractions, rate constants and binding constants. In addition, the extension of this method to complex samples, such as clinical specimens, will be considered.

Studies of drug interactions with alpha1-acid glycoprotein by using on-line immunoextraction and high-performance affinity chromatography

A method that combined on-line immunoextraction with high-performance affinity chromatography was developed to examine the binding of drugs with α₁-acid glycoprotein (AGP). Affinity microcolumns containing immobilized polyclonal anti-AGP antibodies were developed that had a capture efficiency of up to 98.4% for AGP and a binding capacity of 0.72nmol AGP when using a 20mm×2.1mm i.d. microcolumn. These microcolumns were employed in various formats to examine the binding of drugs to normal AGP and AGP that had been adsorbed from serum samples for patients with systemic lupus erythematosus (SLE). Drugs that were screened in zonal elution experiments for their overall binding to these types of AGP included chlorpromazine, disopyramide, imipramine, propranolol, and warfarin. Most of these drugs showed an increase in their binding to the AGP from SLE serum when compared to normal AGP (i.e., an increase of 13-76%); however, disopyramide gave a 21-25% decrease in retention when the same AGP samples were compared. Frontal analysis was used to further evaluate the binding of disopyramide and imipramine to these forms of AGP. Both drugs gave a good fit to a model that involved a combination of saturable and non-saturable interactions with AGP. Changes in the non-saturable interactions accounted for most of variations seen in the binding of disopyramide and imipramine with the AGP samples. The methods used in this study could be adapted for use in personalized medicine and the study of other proteins or drugs using aqueous mixtures or clinical samples.

Data-driven prediction of adverse drug reactions induced by drug-drug interactions

Background

The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects.

Method

We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles.

Results

We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org. We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs via DDIs. This allowed us to identify potential DDI-induced ADRs not yet clinically reported. The ability of the models to quantify adverse effects between drug classes also suggests that we may be able to select drug combinations that minimize the risk of ADRs.

Conclusion

Almost all information on DDI-induced ADRs is generated after drug approval. This situation poses significant health risks for vulnerable patient populations with comorbidities. To help mitigate the risks, we developed a robust probabilistic approach to prospectively predict DDI-induced ADRs. Based on this approach, we developed prediction models for 1,096 ADRs and used them to predict the propensity of all pairwise combinations of nearly 800 drugs to be associated with these ADRs via DDIs. We made the predictions publicly available via internet access.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-017-0153-6) contains supplementary material, which is available to authorized users.