Deciphering the Coevolutionary Dynamics of L2 ß-Lactamases via Deep Learning.

L2 ß-lactamases, serine-based class A ß-lactamases expressed by Stenotrophomonas maltophilia, play a pivotal role in antimicrobial resistance (AMR). However, limited studies have been conducted on these important enzymes. To understand the coevolutionary dynamics of L2 ß-lactamase, innovative computational methodologies, including adaptive sampling molecular dynamics simulations, and deep learning methods (convolutional variational autoencoders and BindSiteS-CNN) explored conformational changes and correlations within the L2 ß-lactamase family together with other representative class A enzymes including SME-1 and KPC-2. This work also investigated the potential role of hydrophobic nodes and binding site residues in facilitating the functional mechanisms. The convergence of analytical approaches utilized in this effort yielded comprehensive insights into the dynamic behavior of the ß-lactamases, specifically from an evolutionary standpoint. In addition, this analysis presents a promising approach for understanding how the class A ß-lactamases evolve in response to environmental pressure and establishes a theoretical foundation for forthcoming endeavors in drug development aimed at combating AMR.

Hong Kong consensus recommendations on the management of pancreatic ductal adenocarcinoma.

This project was undertaken to develop the first set of consensus statements regarding the management of pancreatic ductal adenocarcinoma (PDAC) in Hong Kong, with the goal of providing guidance to local clinicians. A multidisciplinary panel of experts discussed issues surrounding current PDAC management and reviewed evidence gathered in the local context to propose treatment recommendations. The experts used the Delphi approach to finalise management recommendations. Consensus was defined as ≥80% acceptance among all expert panel members. Thirty-nine consensus statements were established. These statements cover all aspects of PDAC management, including diagnosis, resectability criteria, treatment modalities according to resectability, personalised management based on molecular profiling, palliative care, and supportive care. This project fulfils the need for guidance regarding PDAC management in Hong Kong. To assist clinicians with treatment decisions based on varying levels of evidence and clinical experience, treatment options are listed in several consensus statements.

Stevens-Johnson syndrome and toxic epidermal necrolysis in Hong Kong.

INTRODUCTION: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) [hereafter, SJS/TEN] are uncommon but severe mucocutaneous reactions. Although they have been described in many populations worldwide, data from Hong Kong are limited. Here, we explored the epidemiology, disease characteristics, aetiology, morbidity, and mortality of SJS/TEN in Hong Kong. METHODS: This retrospective cohort study included all hospitalised patients who had been diagnosed with SJS/TEN in Prince of Wales Hospital from 1 January 2004 to 31 December 2020. RESULTS: There were 125 cases of SJS/TEN during the 17-year study period. The annual incidence was 5.07 cases per million. The mean age at onset was 51.4 years. The mean maximal body surface area of epidermal detachment was 23%. Overall, patients in 32% of cases required burns unit or intensive care unit admission. Half of the cases involved concomitant sepsis, and 23.2% of cases resulted in multiorgan failure or disseminated intravascular coagulation. The mean length of stay was 23.9 days. The cause of SJS/TEN was attributed to a drug in 91.9% of cases, including 84.2% that involved anticonvulsants, allopurinol, antibiotics, or analgesics. In most cases, patients received treatment comprising either best supportive care alone (35.2%) or combined with intravenous immunoglobulin (43.2%). The in-hospital mortality rate was 21.6%. Major causes of death were multiorgan failure and/or fulminant sepsis (81.5%). CONCLUSION: This study showed that SJS/TEN are uncommon in Hong Kong but can cause substantial morbidity and mortality. Early recognition, prompt withdrawal of offending agents, and multidisciplinary supportive management are essential for improving clinical outcomes.

A small-molecule PI3Kα activator for cardioprotection and neuroregeneration.

Harnessing the potential beneficial effects of kinase signalling through the generation of direct kinase activators remains an underexplored area of drug development1-5. This also applies to the PI3K signalling pathway, which has been extensively targeted by inhibitors for conditions with PI3K overactivation, such as cancer and immune dysregulation. Here we report the discovery of UCL-TRO-1938 (referred to as 1938 hereon), a small-molecule activator of the PI3Kα isoform, a crucial effector of growth factor signalling. 1938 allosterically activates PI3Kα through a distinct mechanism by enhancing multiple steps of the PI3Kα catalytic cycle and causes both local and global conformational changes in the PI3Kα structure. This compound is selective for PI3Kα over other PI3K isoforms and multiple protein and lipid kinases. It transiently activates PI3K signalling in all rodent and human cells tested, resulting in cellular responses such as proliferation and neurite outgrowth. In rodent models, acute treatment with 1938 provides cardioprotection from ischaemia-reperfusion injury and, after local administration, enhances nerve regeneration following nerve crush. This study identifies a chemical tool to directly probe the PI3Kα signalling pathway and a new approach to modulate PI3K activity, widening the therapeutic potential of targeting these enzymes through short-term activation for tissue protection and regeneration. Our findings illustrate the potential of activating kinases for therapeutic benefit, a currently largely untapped area of drug development.

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

Therapeutic Validation of GEF-H1 Using a De Novo Designed Inhibitor in Models of Retinal Disease.

Inflammation and fibrosis are important components of diseases that contribute to the malfunction of epithelia and endothelia. The Rho guanine nucleotide exchange factor (GEF) GEF-H1/ARHGEF-2 is induced in disease and stimulates inflammatory and fibrotic processes, cell migration, and metastasis. Here, we have generated peptide inhibitors to block the function of GEF-H1. Inhibitors were designed using a structural in silico approach or by isolating an inhibitory sequence from the autoregulatory C-terminal domain. Candidate inhibitors were tested for their ability to block RhoA/GEF-H1 binding in vitro, and their potency and specificity in cell-based assays. Successful inhibitors were then evaluated in models of TGFß-induced fibrosis, LPS-stimulated endothelial cell-cell junction disruption, and cell migration. Finally, the most potent inhibitor was successfully tested in an experimental retinal disease mouse model, in which it inhibited blood vessel leakage and ameliorated retinal inflammation when treatment was initiated after disease diagnosis. Thus, an antagonist that blocks GEF-H1 signaling effectively inhibits disease features in in vitro and in vivo disease models, demonstrating that GEF-H1 is an effective therapeutic target and establishing a new therapeutic approach.

Prognostic implication of the neoadjuvant rectal score and other biomarkers of clinical outcome in Hong Kong Chinese patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.

BACKGROUND: Neoadjuvant chemoradiotherapy is a standard treatment for locally advanced rectal cancer, for which pathological complete response is typically used as a surrogate survival endpoint. Neoadjuvant rectal score is a new biomarker that has been shown to correlate with survival. The main objectives of this study were to investigate factors contributing to pathological complete response, to validate the prognostic significance of neoadjuvant rectal score, and to investigate factors associated with a lower neoadjuvant rectal score in a cohort of Hong Kong Chinese. METHODS: Data of patients with locally advanced rectal cancer who received neoadjuvant chemoradiotherapy from August 2006 to October 2018 were retrieved from hospital records and retrospectively analysed. RESULTS: Of 193 patients who had optimal response to neoadjuvant chemoradiotherapy and surgery, tumour down-staging was the only independent prognostic factor that predicted pathological complete response (P<0.0001). Neoadjuvant rectal score was associated with overall survival (hazard ratio [HR]=1.042, 95% confidence interval [CI]=1.021-1.064; P<0.0001), disease-free survival (HR=1.042, 95% CI=1.022-1.062; P<0.0001), locoregional recurrence-free survival (HR=1.070, 95% CI=1.039-1.102; P<0.0001) and distant recurrence-free survival (HR=1.034, 95% CI=1.012-1.056; P=0.002). Patients who had pathological complete response were associated with a lower neoadjuvant rectal score (P<0.0001), but pathological complete response was not associated with survival. For patients with intermediate neoadjuvant rectal scores, late recurrences beyond 72 months from diagnosis were observed. CONCLUSION: Neoadjuvant rectal score is an independent prognostic marker of survival and disease recurrence in a cohort of Hong Kong Chinese patients who received neoadjuvant chemoradiotherapy for locally advanced rectal cancer.

Reversal of behavioural phenotype by the cannabinoid-like compound VSN16R in fragile X syndrome mice.

Fragile X syndrome is the most common inherited intellectual disability and mono-genetic cause of autism spectrum disorder. It is a neurodevelopmental condition occurring due to a CGG trinucleotide expansion in the FMR1 gene. Polymorphisms and variants in large-conductance calcium-activated potassium channels are increasingly linked to intellectual disability and loss of FMR protein causes reduced large-conductance calcium-activated potassium channel activity leading to abnormalities in synapse function. Using the cannabinoid-like large-conductance calcium-activated potassium channel activator VSN16R we rescued behavioural deficits such as repetitive behaviour, hippocampal dependent tests of daily living, hyperactivity and memory in a mouse model of fragile X syndrome. VSN16R has been shown to be safe in a phase 1 study in healthy volunteers and in a phase 2 study in patients with multiple sclerosis with high oral bioavailability and no serious adverse effects reported. VSN16R could therefore be directly utilized in a fragile X syndrome clinical study. Moreover, VSN16R showed no evidence of tolerance, which strongly suggests that chronic VSN16R may have great therapeutic value for fragile X syndrome and autism spectrum disorder. This study provides new insight into the pathophysiology of fragile X syndrome and identifies a new pathway for drug intervention for this debilitating disorder.

Peptides Derived from Vascular Endothelial Growth Factor B Show Potent Binding to Neuropilin-1.

Vascular endothelial growth factors (VEGFs) regulate significant pathways in angiogenesis, myocardial and neuronal protection, metabolism, and cancer progression. The VEGF-B growth factor is involved in cell survival, anti-apoptotic and antioxidant mechanisms, through binding to VEGF receptor 1 and neuropilin-1 (NRP1). We employed surface plasmon resonance technology and X-ray crystallography to analyse the molecular basis of the interaction between VEGF-B and the b1 domain of NRP1, and developed VEGF-B C-terminus derived peptides to be used as chemical tools for studying VEGF-B - NRP1 related pathways. Peptide lipidation was used as a means to stabilise the peptides. VEGF-B-derived peptides containing a C-terminal arginine show potent binding to NRP1-b1. Peptide lipidation increased binding residence time and improved plasma stability. A crystal structure of a peptide with NRP1 demonstrated that VEGF-B peptides bind at the canonical C-terminal arginine binding site. VEGF-B C-terminus imparts higher affinity for NRP1 than the corresponding VEGF-A165 region. This tight binding may impact on the activity and selectivity of the full-length protein. The VEGF-B167 derived peptides were more effective than VEGF-A165 peptides in blocking functional phosphorylation events. Blockers of VEGF-B function have potential applications in diabetes and non-alcoholic fatty liver disease.

Repeat hepatic resection versus radiofrequency ablation for recurrent hepatocellular carcinoma: retrospective multicentre study.

BACKGROUND: The therapeutic value of repeat hepatic resection (rHR) or radiofrequency ablation (RFA) for recurrent hepatocellular carcinoma (HCC) is unknown. This study aimed to investigate the safety and efficacy of rHR or RFA. METHODS: This was a retrospective multicentre study of patients with recurrent HCC within the Milan criteria who underwent rHR or RFA at nine university hospitals in China and Italy between January 2003 and January 2018. Survival after rHR or RFA was examined in unadjusted analyses and after propensity score matching (1 : 1). RESULTS: Of 847 patients included, 307 and 540 underwent rHR and RFA respectively. Median overall survival was 73.5 and 67.0 months after rHR and RFA respectively (hazard ratio 1.01 (95 per cent c.i. 0.81 to 1.26)). Median recurrence-free survival was longer after rHR versus RFA (23.6 versus 15.2 months; hazard ratio 0.76 (95 per cent c.i. 0.65 to 0.89)). These results were confirmed after propensity score matching. RFA was associated with lower morbidity of grade 3 and above (0.6 versus 6.2 per cent; P < 0.001) and shorter hospital stay (8.0 versus 3.0 days, P < 0.001) than rHR. CONCLUSION: rHR was associated with longer recurrence-free survival but not overall survival compared with RFA.

A descriptive evaluation of resident prescription writing errors and self-perceived learning needs in dermatology.

Prescribing errors in medical practice are common, and may be preventable in a significant proportion of cases. The literature on dermatological prescription errors is scarce. We sought to determine the rate and causes of resident prescribing errors in an outpatient dermatology practice, and surveyed residents' self-perceived prescription writing learning needs. All prescription errors were tabulated at the Ricky Kanee Schachter Dermatology Clinic (Women's College Hospital) from November 2019 to January 2020. There was an overall prescribing error rate of 1.58% (23/1457), with no significant difference between topical and systemic drugs (1.85% and 0.86%, respectively; P = 0.20) or between written prescriptions and those created by the electronic medical record (1.66% and 1.29%, respectively; P = 0.84). The survey response rate was 26.2% (22/82), with respondents reporting their overall confidence in dermatology prescription writing as (mean ± SD) 7.14 ± 1.75 out of 10. While the resident prescribing error rate was relatively low, multiple errors were avoidable, and residents agree that targeted dermatology-specific training in prescription writing is needed.

Design and synthesis of Coenzyme A analogues as Aurora kinase A inhibitors: An exploration of the roles of the pyrophosphate and pantetheine moieties.

Coenzyme A (CoA) is a highly selective inhibitor of the mitotic regulatory enzyme Aurora A kinase, with a novel mode of action. Herein we report the design and synthesis of analogues of CoA as inhibitors of Aurora A kinase. We have designed and synthesised modified CoA structures as potential inhibitors, combining dicarbonyl mimics of the pyrophosphate group with a conserved adenosine headgroup and different length pantetheine-based tail groups. An analogue with a -SH group at the end of the pantotheinate tail showed the best IC50, probably due to the formation of a covalent bond with Aurora A kinase Cys290.

In crystallo-screening for discovery of human norovirus 3C-like protease inhibitors.

Outbreaks of human epidemic nonbacterial gastroenteritis are mainly caused by noroviruses. Viral replication requires a 3C-like cysteine protease (3CLpro) which processes the 200 kDa viral polyprotein into six functional proteins. The 3CLpro has attracted much interest due to its potential as a target for antiviral drugs. A system for growing high-quality crystals of native Southampton norovirus 3CLpro (SV3CP) has been established, allowing the ligand-free crystal structure to be determined to 1.3 Å in a tetrameric state. This also allowed crystal-based fragment screening to be performed with various compound libraries, ultimately to guide drug discovery for SV3CP. A total of 19 fragments were found to bind to the protease out of the 844 which were screened. Two of the hits were located at the active site of SV3CP and showed good inhibitory activity in kinetic assays. Another 5 were found at the enzyme's putative RNA-binding site and a further 11 were located in the symmetric central cavity of the tetramer.

ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees.

SUMMARY: ScaffoldGraph (SG) is an open-source Python library and command-line tool for the generation and analysis of molecular scaffold networks and trees, with the capability of processing large sets of input molecules. With the increase in high-throughput screening data, scaffold graphs have proven useful for the navigation and analysis of chemical space, being used for visualization, clustering, scaffold-diversity analysis and active-series identification. Built on RDKit and NetworkX, SG integrates scaffold graph analysis into the growing scientific/cheminformatics Python stack, increasing the flexibility and extendibility of the tool compared to existing software. AVAILABILITY AND IMPLEMENTATION: SG is freely available and released under the MIT licence at https://github.com/UCLCheminformatics/ScaffoldGraph.

Potent non-hydroxamate inhibitors of histone deacetylase-8: Role and scope of an isoindolin-2-yl linker with an α-amino amide as the zinc-binding unit.

A series of potent inhibitors of histone deacetylase-8 (HDAC8) is described that contains an α-amino amide zinc-binding unit and a substituted isoindolinyl capping group. The presence of a 2,4-dichlorophenyl unit located in the acetate-release cavity was shown to confer a gain of approx. 4.3 kJ mol-1 in binding energy compared to a phenyl group, and the isoindoline linker has approx. 5.8 kJ mol-1 greater binding energy than the corresponding tetrahydroisoquinoline ring system. In a series of 5-substituted isoindolin-2-yl inhibitors, a 5-acetylamino derivative was found to be more potent than the 5-unsubstituted lead HDAC8 inhibitor (increase in binding energy of 2.0 kJ mol-1, ascribed to additional binding interactions within the Nε-acetyl-l-lysine binding tunnel in HDAC8, including hydrogen bonding to Asp101. Tolerance of a 5-substituent (capping group) on the isoindoline ring has been demonstrated, and which in some cases confers improved enzyme inhibition, the HDAC8 substrate-binding region providing a platform for additional interactions.

Modelling the binding mode of macrocycles: Docking and conformational sampling.

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.