Pharmacokinetics Modeling and Molecular Modeling of Drug-Drug Interactions Between Opioids and Benzodiazepines

The potential drug-drug interactions (DDIs) of concurrent use of opioids and benzodiazepines have aroused high attention in the world for the severe side effects when two types of drugs are co-administered. However, there is much unknown in the DDI between these two kinds of drugs. The objective of this project is to find out the mechanism underlying the DDIs between opioids and benzodiazepines. There are two basic factors can contribute to the interactions, pharmacokinetic (PK) interaction and pharmacodynamic (PD) interaction. PK interaction is one of the most common reasons that lead to DDI. This kind of interaction may occur when two drugs are metabolized by the same Cytochrome P450 enzymes. In this work, we quantitatively predicted the DDI between oxycodone and diazepam through empirical PK modeling, minimal physiologically-based PK (PBPK) modeling and full PBPK modeling. Another possibility causing the DDI is PD interaction. In PD study, we used molecular modeling techniques including molecular docking, molecular dynamics simulations and MM/PBSA calculations to predict the pharmacodynamic interaction between opioids and benzodiazepines. The results of PK interaction study indicated that benzodiazepines have limited inhibitory effect on opioids and the extent of inhibition slightly increased with the overdose of benzodiazepines. Usually PK interactions might only be observed when highly increasing the dosage of benzodiazepines. The results of PD interaction study indicated that benzodiazepines may act as agonists or antagonists of the µ- and k-opioid receptors. We concluded that PD interaction is likely to play a more important role in DDIs between opioids and benzodiazepines

Texture Profile Analysis of Sliced Cheese in relation to Chemical Composition and Storage Temperature

The quantitative relationships among chemical composition, storage temperature, and texture of cheese were not fully understood. In this study, the effects of composition and temperature on textural properties of eight common varieties of sliced cheese were examined. The textural properties of sliced cheeses, including firmness, cohesiveness, adhesiveness, springiness, chewiness, and resilience, were measured by texture profile analysis after storage at 4 and 25 ∘ C for 4 h. Multivariate logistic regression models were established to describe the quantitative relationships of textural properties (dependent variables) to chemical composition and storage temperature (independent variables) of sliced cheeses. Results showed that protein, fat, moisture, and sodium chloride contents as well as storage temperature significantly affected the texture of sliced cheeses ( < 0.05). In particular, fat in the dry matter and moisture in the nonfat substances were negatively correlated with firmness of sliced cheeses ( < 0.05). As storage temperature rose from 4 to 25 ∘ C, the average values of firmness, chewiness, and resilience substantially declined by 42%, 45%, and 17%, respectively ( < 0.05). This study provided reference data for adjusting chemical composition and storage temperature of common cheese products to obtain favorable texture for Chinese consumers, which thereby facilitated the localization of cheese industry in Chinese market

Applications of ML/DL overcoming current challenges in structure-based drug design

In the rapidly evolving landscape of drug discovery and development, the application of structure-based drug design (SBDD) methods has become increasingly crucial. Despite the advancements in this field, several challenges continue to impede progress, particularly in the areas of binding heterogeneity, targeted ligand development, and the exploration of the vast chemical space for drug-like molecules. This dissertation addresses these challenges by integrating traditional SBDD techniques with advanced machine learning (ML) or deep learning (DL) methodologies, paving the way for more efficient and targeted drug discovery processes. The research began with a focus on cannabinoid receptors, employing a set of molecular modeling methods, to predict binding affinity and selectivity of compounds. This approach aids in identifying key interaction sites, facilitating the development of potent and selective ligands. The study also explored complexities of drug-drug interactions, providing insights into the dynamic interplay of pharmacodynamic and pharmacokinetic factors. To combat the issue of binding heterogeneity, novel computational approaches were introduced. One involves the creation of an advanced computational algorithm that combines structure-based docking scores with ligand-based structural similarities. Another is the development of an ML-based scoring function using ligand-residue interaction profiles. These innovative methods have shown a marked improvement in the accuracy of binding affinity predictions compared to traditional techniques. A function-based screening methodology was also presented to predict the functionality of CB2 ligands. The success of this approach in the development of CB2 agonists, with a significant success rate, demonstrates its potential in overcoming challenges in ligand design and fulfilling specific biological functions. Additionally, the dissertation introduced DRUG-GAN, a deep learning model for the de novo generation of molecular structures. This model, when integrated with similarity search methods, significantly narrows the chemical search space and accelerates the process of lead compound identification in the drug discovery and development. In conclusion, this research successfully demonstrates the effectiveness of combining computational modeling techniques with artificial intelligence in addressing key challenges in SBDD. The methodologies developed herein not only enhance the precision of drug design but also expand the horizons for discovering novel drug candidates, setting a new benchmark in the field of pharmaceutical research

United Nations Humanitarian Air Service: Network Optimization: A Tabu Search Approach

Network scheduling and fleet assignment are essential tasks for airline operation. In order to generate an optimal flight plan, the flight route and the flight schedule of each aircraft in the fleet requires deliberated consideration and planning. Compared with commercial airlines, which are in pursuit of maximal benefit during the operations, the humanitarian air services have different goals and therefore different strategies are designed. The humanitarian air service dedicates to fulfilling maximal passenger requests by reacting in a relatively short time frame, and the overall cost efficiency needs to be maximised. In this research, the United Nations Humanitarian Service’s (UNHAS) South Sudan mission is taken as the case to study and a metaheuristic method on top of the multi-integer linear programming (MILP) model is designed to solve the optimisation problem. The optimisation process consists of two stages: the tabu search process to assign the flight routes among the fleet, and a variation of a Fleet Size and Mix Vehicle Routing Problem (FSMVRP) model to finally determine the time schedule of each aircraft. The model is able to unlimitedly split the passenger requests and recaptures passenger spillage. It considers much fewer assumptions during the solving process and it provides large flexibility for the planner to manually modify the model based on their purpose. A dynamic balance of aircraft utilisation time regarding the Minimum Guaranteed Hours (MGH) within the fleet is also discussed. The result of this method is compared with the previous study of S.P. Niemansburg, which shows 1% to 11% of cost saved on a single day's operation regarding different levels of passenger spillage.Aerospace Engineerin

semantic based approximate query across multiple ontologies

IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn ChinaIn this paper, we propose an approach for better ontology interoperability using approximation technology of semantic terminology across multiple ontolgies. We use description logic language for describing ontological information and perform

towards scalable processing for a large-scale ride sharing service

Ride sharing is a promising way to realize a convenient, economic and low-carbon travel. After analyzing and refining the requirements of a ride sharing service, the paper models the trajectory matching therein and discusses the implementation of a large-scale ride sharing service with the aim of improving the efficiency and scalability.Kyushu Sangyo Univ (KSU), IEEE, IEEE Comp Soc, IEEE Tech Comm Scalable Comp (TCSC), Informat Proc Soc Japan (IPSJ), Inst Elect, Informat & Commun Engineers (IEICE), FCVB, IPSJ Special Interest Grp Distributed Proc Syst (IPSJ SIG-DPS), IEICE Special Interest Grp Dependable Comp (IEICE SIG-DC), IPSJ Special Interest Grp Comp Secur (IPSJ SIG-CSEC), IPSJ Special Interest Grp Mobile Comp & Ubiquitous Commun (IPSJ SIG-MBL)Ride sharing is a promising way to realize a convenient, economic and low-carbon travel. After analyzing and refining the requirements of a ride sharing service, the paper models the trajectory matching therein and discusses the implementation of a large-scale ride sharing service with the aim of improving the efficiency and scalability