Microbial Degradation of Benzene Derivatives

The thesis is divided into two parts. Both deal with the microbial metabolism of benzene derivatives but in different aspects. Part I attempts to elucidate some new mechanisms and stereochemistry involved in muconic acid pathways, which commonly occur in the microbial degradation of benzene derivatives. To investigate the conversion of 4-methyl into 3-methyl-muconolactone, three specially labelled muconolactones were tested with cell-free extracts of Rhodococcus rhodocrous. The results showed that the conversion of 4-methyl to 3-methylmuconolactone proceeds through two steps. Firstly, enzyme catalyses the formation of the new lactone ring by anti addition. Secondly the original lactone ring is opened enzymically by anti elimination. Dilactone was shown to be an intermediate. Studies on the inhibitors of the methylisomerase have been carried out from chemical point of view. Among the substituents studied, the larger substituents did not affect the biotransformation of pyrocatechols into corresponding muconolactones in Pseudomonas putida but affected those in Aspergillus niger. It was shown that the optical active 4-ethylmuconolactone can cyclise under mild condition giving the dilactone with opposite optical rotation, but 3,4-dimethylmuconolactone can not be cyclised under various conditions. In part II, it was hoped to delineate some influences of fluorine substituents on the biosynthesis of cyclopenin group of benzodiazepine alkaloids. Three fluoro-phenylalanines were tested with the fungus Penicillium cyclopium. The qualitative results obtained showed that these substrates have been incorporated into benzodiazepine alkaloids with low yields

Ligand-based virtual screening using binary kernel discrimination

This paper discusses the use of a machine-learning technique called binary kernel discrimination (BKD) for virtual screening in drug- and pesticide-discovery programmes. BKD is compared with several other ligand-based tools for virtual screening in databases of 2D structures represented by fragment bit-strings, and is shown to provide an effective, and reasonably efficient, way of prioritising compounds for biological screening

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

Combined inhibition of the Fanconi anaemia (FA) pathway and ATR promotes R-loop generation and profound radiosensitisation in glioblastoma

Glioblastoma is a deadly cancer in which treatment resistance is mediated through extensive intratumoural heterogeneity including difficult-to-treat glioblastoma stem cell (GSC) subpopulations. GSC eradication represents an attractive therapeutic goal, but these cells possess upregulated DNA damage response (DDR) processes, resulting in a chemo- and radioresistant phenotype. However, recent studies have demonstrated that elevated replication stress in GSCs may partially explain DDR upregulation and resistance, thus highlighting a potential therapeutically exploitable vulnerability. ATR and the FA-pathway are both fundamental to cellular DNA replication stress responses and maintaining replication fork stability. Since we have previously shown the FA-pathway is inactive in normal brain, but is re-activated in glioblastoma with potential to provide a cancer-specific foundation for combination DDR therapies, we explored the therapeutic potential of simultaneous inhibition of the FA-pathway (FAPi) and ATR (ATRi), in addition to other FA-pathway-based DDR inhibitor (DDRi) combinations. We find that compared with single agent treatments, combined inhibition of the FA-pathway and ATR in both 2D and 3D GSC ex vivo models promotes a substantial increase in conflicts between DNA replication and transcription (R-loops) which is further exacerbated by ionising radiation (IR). Molecular analyses of DNA damage indicate that FAPi+ATRi increases peak DNA damage post-IR treatments, with sustained elevation of DNA damage even at 24 hours post-treatment. In conclusion, simultaneously targeting the FA-pathway and ATR represents an appealing therapeutic strategy for glioblastoma. This approach promotes substantial R-loop generation, likely through exacerbating constitutively high levels of DNA replication stress previously observed in GSCs, with deleterious effects in these treatment resistant cells. Our findings underline the value of developing clinical FA pathway inhibitors and also support the application of current ATR inhibitors to molecularly-selected subsets of glioblastoma, namely, those with defects in one of 22 currently known FA-pathway genes which include BRCA1/FANCS and BRCA2/FANCD1

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Glucosylpolyphenols as Inhibitors of Aβ-Induced Fyn Kinase Activation and Tau Phosphorylation: Synthesis, Membrane Permeability, and Exploratory Target Assessment within the Scope of Type 2 Diabetes and Alzheimer's Disease

Despite the rapidly increasing number of patients suffering from type 2 diabetes, Alzheimer's disease, and diabetes-induced dementia, there are no disease-modifying therapies that are able to prevent or block disease progress. In this work, we investigate the potential of nature-inspired glucosylpolyphenols against relevant targets, including islet amyloid polypeptide, glucosidases, and cholinesterases. Moreover, with the premise of Fyn kinase as a paradigm-shifting target in Alzheimer's drug discovery, we explore glucosylpolyphenols as blockers of Aβ-induced Fyn kinase activation while looking into downstream effects leading to Tau hyperphosphorylation. Several compounds inhibit Aβ-induced Fyn kinase activation and decrease pTau levels at 10 μM concentration, particularly the per-O-methylated glucosylacetophloroglucinol and the 4-glucosylcatechol dibenzoate, the latter inhibiting also butyrylcholinesterase and β-glucosidase. Both compounds are nontoxic with ideal pharmacokinetic properties for further development. This work ultimately highlights the multitarget nature, fine structural tuning capacity, and valuable therapeutic significance of glucosylpolyphenols in the context of these metabolic and neurodegenerative disorders

Pharmacokinetics/pharmacodynamics of polymyxin B in patients with bloodstream infection caused by carbapenem-resistant Klebsiella pneumoniae

Introduction: Polymyxin B is a last-line therapy for carbapenem-resistant microorganisms. However, a lack of clinical pharmacokinetic/pharmacodynamic (PK/PD) data has substantially hindered dose optimization and breakpoint setting.Methods: A prospective, multi-center clinical trial was undertaken with polymyxin B [2.5 mg/kg loading dose (3-h infusion), 1.25 mg/kg/12 h maintenance dose (2-h infusion)] for treatment of carbapenem-resistant K. pneumoniae (CRKP) bloodstream infections (BSI). Safety, clinical and microbiological efficacy were evaluated. A validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was applied to determine the concentrations of polymyxin B in blood samples. Population pharmacokinetic (PK) modeling and Monte Carlo simulations were conducted to examine the susceptibility breakpoint for polymyxin B against BSI caused by CRKP.Results: Nine patients were enrolled and evaluated for safety. Neurotoxicity (5/9), nephrotoxicity (5/9), and hyperpigmentation (1/9) were recorded. Blood cultures were negative within 3 days of commencing therapy in all 8 patients evaluated for microbiological efficacy, and clinical cure or improvement occurred in 6 of 8 patients. Cmax and Cmin following the loading dose were 5.53 ± 1.80 and 1.62 ± 0.41 mg/L, respectively. With maintenance dosing, AUCss,24 h was 79.6 ± 25.0 mg h/L and Css,avg 3.35 ± 1.06 mg/L. Monte Carlo simulations indicated that a 1 mg/kg/12-hourly maintenance dose could achieve >90% probability of target attainment (PTA) for isolates with minimum inhibitory concentration (MIC) ≤1 mg/L. PTA dropped substantially for MICs ≥2 mg/L, even with a maximally recommended daily dose of 1.5 mg/kg/12-hourly.Conclusion: This is the first clinical PK/PD study evaluating polymyxin B for BSI. These results will assist to optimize polymyxin B therapy and establish its breakpoints for CRKP BSI

Effect of missing data on multitask prediction methods

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises

A synergistic ozone-climate control to address emerging ozone pollution challenges

Tropospheric ozone threatens human health and crop yields, exacerbates global warming, and fundamentally changes atmospheric chemistry. Evidence has pointed toward widespread ozone increases in the troposphere, and particularly surface ozone is chemically complex and difficult to abate. Despite past successes in some regions, a solution to new challenges of ozone pollution in a warming climate remains unexplored. In this perspective, by compiling surface measurements at ∼4,300 sites worldwide between 2014 and 2019, we show the emerging global challenge of ozone pollution, featuring the unintentional rise in ozone due to the uncoordinated emissions reduction and increasing climate penalty. On the basis of shared emission sources, interactive chemical mechanisms, and synergistic health effects between ozone pollution and climate warming, we propose a synergistic ozone-climate control strategy incorporating joint control of ozone and fine particulate matter. This new solution presents an opportunity to alleviate tropospheric ozone pollution in the forthcoming low-carbon transition.This study was supported by the Research Grants Council of Hong Kong Special Administrative Region via General Research Funds (HKBU 15219621 and PolyU 15212421) and a Theme-based Research Scheme (T24-504/17-N). The authors acknowledge the support of the Australia–China Centre on Air Quality Science and Management. R.S. acknowledges support from ANID/FONDAP/1522A0001. D.S. thanks the program of Coordination for the Improvement of Higher Education Personnel (CAPES) (436466/2018-0). X.X. acknowledges funding from the Natural Science Foundation of China (41330422) and the Chinese Academy of Meteorological Sciences (2020KJ003). K.L. is supported by the Natural Science Foundation of China (42205114), Jiangsu Carbon Peak and Neutrality Science and Technology Innovation fund (BK20220031), and the Startup Foundation for Introducing Talent of NUIST. We sincerely appreciate all the organizations and programs introduced in the section “experimental procedures” for freely providing ozone data. We thank Dr. Owen Cooper (University of Colorado, Boulder, and NOAA) for insightful guidance and discussion. No organization or program will be responsible for the results generated from their data.Peer reviewe