Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation

A biodegradable and flexible neural interface for transdermal optoelectronic modulation and regeneration of peripheral nerves

Optoelectronic neural interfaces can leverage the photovoltaic effect to convert light into electrical current, inducing charge redistribution and enabling nerve stimulation. This method offers a non-genetic and remote approach for neuromodulation. Developing biodegradable and efficient optoelectronic neural interfaces is important for achieving transdermal stimulation while minimizing infection risks associated with device retrieval, thereby maximizing therapeutic outcomes. We propose a biodegradable, flexible, and miniaturized silicon-based neural interface capable of transdermal optoelectronic stimulation for neural modulation and nerve regeneration. Enhancing the device interface with thin-film molybdenum significantly improves the efficacy of neural stimulation. Our study demonstrates successful activation of the sciatic nerve in rodents and the facial nerve in rabbits. Moreover, transdermal optoelectronic stimulation accelerates the functional recovery of injured facial nerves.

Utility of sustainable ratio derivative spectrophotometry for the concurrent assay of synergistic repurposed drugs for COVID-19 infections; Insilico pharmacokinetics proof

The cutting-edge combination of fluvoxamine (FVM) and ivermectin (IVM) has been presented as a proposed dosage form for the treatment of COVID-19 infections in early diagnosed patients. The main objective of this work is to develop simple, sensitive, and efficient methods for the synchronous quantification of FVM and IVM without any prior separation. Four green UV-methods were employed for the synchronous quantification, namely: Fourier functions convolution of absorption spectra, FFAS, Fourier functions convolution of derivative spectra of absorption curves, FFDS, Fourier function convolution of ratio spectra of absorption curves, FFRS and the dual-wavelength method, DWM. FFRS and DWM approaches can be able to reconcile the two components' significantly interfering spectrum presented in this commixture. Good linearity was checked in the range of 5-40, and 2.5-25 μg/mL for the FVM, and IVM, respectively. All approaches developed have been recommended in compliance with ICH principles. Furthermore, the approaches' greenness was predestined by "National Environmental Method Index" (NEMI), "Analytical GREEnness metric (AGREE)", the "Analytical Eco-Scale", and the "Green Analytical Procedure Index" (GAPI). In addition, spider diagram was utilized for the assessment of the greenness index of the solvent used. Beside greenness, the sustainability of our methods was investigated using the HEXAGON tool. Continuing the constant pursuit of greenness, drug-drug interactions (DDIs) between FVM & IVM were predicted by insilico tools to ensure the safety of the suggested mixture as a preliminary step before invitro and in vivo studies. Because they were deemed sustainable, affordable, and successful, the suggested UV-methods may be used for routine quality control investigations of the indicated formulations FVM & IVM.

Caffeic acid, a dietary polyphenol, pre-sensitizes pancreatic ductal adenocarcinoma to chemotherapeutic drug

Resistance to chemotherapeutics is an eminent cause that leads to search for options that help in diminution of pancreatic ductal adenocarcinoma (PDAC) by overcoming resistance issues. Caffeic acid (CFA), a polyphenol occurring in many dietary foods, is known to show antidiabetic and anticancer properties potential. To unveil the effect of CFA on PDAC, we carried out this research in PDAC cells, following which we checked the combination effect of CFA and chemotherapeutics and pre-sensitization effects of CFA. Multitudinous web-based approaches were applied for identifying CFA targets in PDAC and then getting their interconnections. Subsequently, we manifested CFA effects by in-vitro analysis showing IC50 concentrations of 37.37 and 15.06 µM on Panc-1 and Mia-PaCa-2, respectively. The combination index of CFA with different drugs was explored which showed the antagonistic effects of combination treatment leading to further investigation of the pre-sensitizing effects. CFA pre-sensitization reduced IC50 concentration of doxorubicin in both PDAC cell lines which also triggered ROS generation determined by 2',7'-dichlorofluorescin diacetate assay. The differential gene expression analysis after CFA treatment showed discrete genes affected in both cells, i.e. N-Cad and Cas9 in Panc-1 and Pi3K/AkT/mTOR along with p53 in Mia-PaCa-2. Collectively, this study investigated the role of CFA as PDAC therapeutics and explored the mechanism in mitigating resistance of PDAC by sensitizing to chemotherapeutics.Communicated by Ramaswamy H. Sarma.

Prediction of Adverse Effects of Drug-Drug Interactions on Cardiovascular System Based on the Analysis of Structure-Activity Relationships

Co-administration of drugs often leads to drug-drug interactions, which could be accompanied by various adverse drug reactions that pose a threat to life and health of the patient. The effect caused by adverse drug reactions on cardiovascular system is one of the most significant manifestations of drug-drug interaction. Clinical assessment of adverse drug reactions resulting from drug-drug interaction between all drug pairs used in therapeutic practice is not possible. The purpose of this work was to build models using structure-activity analysis to predict adverse effects of drugs on cardiovascular system, mediated by pairwise interactions between the drug pairs when they are taken together. Data on the adverse effects resulting from drug-drug interaction were obtained from the DrugBank database. The data on drug pairs that do not cause such effects, which are necessary for building accurate structure-activity models, were obtained from the TwoSides database, which contains the results of analysis of the spontaneous reports. Two types of descriptors were used to describe a pair of drug structures: PoSMNA descriptors and probabilistic estimates of the prediction of biological activities obtained using the PASS program. Structure-activity relationships were established using the Random Forest method. Prediction accuracy was calculated by means of five-fold cross-validation. The highest accuracy values were obtained using PASS probabilistic estimates as descriptors. The area under the ROC curve was 0.94 for bradycardia, 0.96 for tachycardia, 0.90 for arrhythmia, 0.90 for ECG QT prolongation, 0.91 for hypertension, 0.89 for hypotension.

Bioresorbable thin-film silicon diodes for the optoelectronic excitation and inhibition of neural activities

Neural activities can be modulated by leveraging light-responsive nanomaterials as interfaces for exerting photothermal, photoelectrochemical or photocapacitive effects on neurons or neural tissues. Here we show that bioresorbable thin-film monocrystalline silicon pn diodes can be used to optoelectronically excite or inhibit neural activities by establishing polarity-dependent positive or negative photovoltages at the semiconductor/solution interface. Under laser illumination, the silicon-diode optoelectronic interfaces allowed for the deterministic depolarization or hyperpolarization of cultured neurons as well as the upregulated or downregulated intracellular calcium dynamics. The optoelectronic interfaces can also be mounted on nerve tissue to activate or silence neural activities in peripheral and central nervous tissues, as we show in mice with exposed sciatic nerves and somatosensory cortices. Bioresorbable silicon-based optoelectronic thin films that selectively excite or inhibit neural tissue may find advantageous biomedical applicability.

Predicting drug-drug interactions by electrochemically driven cytochrome P450 3A4 reactions

OBJECTIVES

Human cytochrome P450 3A4 is the most abundant hepatic and intestinal Phase I enzyme that metabolizes approximately 60% marketed drugs. Simultaneous administration of several drugs may result in appearance of drug-drug interaction. Due to the great interest in the combination therapy, the exploration of the role of drug as "perpetrator" or "victim" is important task in pharmacology. In this work the model systems based on electrochemically driven cytochrome P450 3A4 for the analysis of drug combinations was used. We have shown that the analysis of electrochemical parameters of cytochrome P450 3A4 and especially, potential of the start of catalysis, Eonset, possess predictive properties in the determination of the leading ("perpetrator") properties of drug. Based on these experimental data, we concluded, that the more positive potential of the start of catalysis, Eonset, the more pronounced the role of drug as leading medication.

METHODS

Electrochemically driven cytochrome P450 3A4 was used as probe and measuring tool for the estimation of the role of interacting drugs.

RESULTS

It is shown that the electrochemical non-invasive model systems for monitoring the catalytic activity of cytochrome P450 3A4 can be used as prognostic devise in assessment of drug/drug interacting medications.

CONCLUSIONS

Cytochrome P450 3A4 activity was studied in electrochemically driven system. Method was implemented to monitor drug/drug interactions. Based on the obtained experimental data, we can conclude that electrochemical parameter such as potential of onset of catalysis, Eonset, has predictive efficiency in assessment of drug/drug interacting medications in the case of the co-administration.

Methylophiopogonanone A is a naturally occurring broad-spectrum inhibitor against human UDP-glucuronosyltransferases: Inhibition behaviours and implication in herb-drug interactions

Methylophiopogonanone A (MOA) is an abundant homoisoflavonoid in the Chinese herb Ophiopogonis Radix. Recent investigations revealed that MOA inhibited several human cytochrome P450 enzymes (CYPs) and stimulated OATP1B1. However, the inhibitory effects of MOA on phase II drug-metabolizing enzymes, such as human UDP-glucuronosyltransferases (hUGTs), have not been well investigated. Herein, the inhibition potentials of MOA on hUGTs were assessed. The results clearly demonstrated that MOA dose-dependently inhibited all tested hUGTs including UGT1A1 (IC₅₀  = 1.23 μM), one of the most important detoxification enzymes in humans. Further investigations showed that MOA strongly inhibited UGT1A1-catalysed NHPH-O-glucuronidation in a range of biological settings including hUGT1A1, human liver microsomes (HLM) and HeLa cells overexpressing UGT1A1. Inhibition kinetic analyses demonstrated that MOA competitively inhibited UGT1A1-catalysed NHPH-O-glucuronidation in both hUGT1A1 and HLM, with K_i values of 0.52 and 1.22 μM, respectively. Collectively, our findings expanded knowledge of the interactions between MOA and human drug-metabolizing enzymes, which would be very helpful for guiding the use of MOA-related herbal products in clinical settings.

In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms

Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

Recent Trends in Enzyme Inhibition and Activation in Drug Design

It is known that enzymes are involved in many pathological conditions, such as inflammation, diabetes, microbial infections, HIV, neoplastic, neglected diseases and others [...]

[Modeling of drug-drug interactions between omeprazole and erythromycin with cytochrome P450 3A4 in vitro assay]

In the present study the electrochemical system based on recombinant cytochrome P450 3A4 (CYP3A4) was used for the investigation of potential drug-drug interaction between medicinal preparations employed for Helicobacter pylori eradication therapy. Drug interactions were demonstrated in association of omeprazole as a proton pump inhibitor (PPI) and macrolide antibiotic erythromycin during cytochrome P450 3A4-mediated metabolism. It was shown that in the presence of omeprazole the rate of N-demethylase activity of CYP3A4 to erythromycin measured by means of product (formaldehyde) formation decreased. Mass-spectrometry analysis of omeprazole sulfone as a CYP3A4-mediated metabolite demonstrated the absence of erythromycin influence on CYP3A4-dependent omeprazole metabolism. This phenomenon may be explained by lower spectral dissociation constant of CYP3A4-omeprazole complex (Kd = 18±2 μM) than that of CYP3A4-erythromycin complex (Kd = 52 μM). Using the electrochemical model of electrochemically-driven drug metabolism it is possible to register CYP3A4-mediated catalytic conversion of certain drugs. In vitro experiments of potential CYP3A4-mediated drug-drug interactions are in accordance with in silico modeling with program PASS and PoSMNA descriptors in the case of omeprazole/erythromycin combinations.