An efficient hybrid deep learning architecture for predicting short antimicrobial peptides.

Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.

eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning.

Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70-0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.

Trimethylene Methane Dianion Equivalent for the Asymmetric Consecutive Allylation of Aldehydes: Applications to Prins-Driven Macrocyclizations for the Synthesis of Bryostatin 1 and Analogues.

We report a one-step (one-flask) generation and reaction of a bifunctional allylating reagent, a trimethylene methane dianion equivalent, that provides a route for the asymmetric 2-(trimethylsilylmethyl) allylation of aldehydes. The product of the first aldehyde allylation process is then set to engage in a second separate aldehyde allylation, providing an improved Prins macrocyclization strategy both for the scalable synthesis of bryostatin 1 and for the total synthesis of a new potent bryostatin analogue.

Practical synthesis of the therapeutic leads tigilanol tiglate and its analogues.

Tigilanol tiglate is a natural product diterpenoid in clinical trials for the treatment of a broad range of cancers. Its unprecedented protein kinase C isoform selectivity make it and its analogues exceptional leads for PKC-related clinical indications, which include human immunodeficiency virus and AIDS eradication, antigen-enhanced cancer immunotherapy, Alzheimer's disease and multiple sclerosis. Currently, the only source of tigilanol tiglate is a rain forest tree, Fontainea picrosperma, whose limited number and restricted distribution (northeastern Australia) has prompted consideration of designed tree plantations to address supply needs. Here we report a practical laboratory synthesis of tigilanol tiglate that proceeds in 12 steps (12% overall yield, >80% average yield per step) and can be used to sustainably supply tigilanol tiglate and its analogues, the latter otherwise inaccessible from the natural source. The success of this synthesis is based on a unique strategy for the installation of an oxidation pattern common to many biologically active tiglianes, daphnanes and their analogues.

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition.

Transcription factors (TFs) play an important role in gene expression and regulation of 3D genome conformation. TFs have ability to bind to specific DNA fragments called enhancers and promoters. Some TFs bind to promoter DNA fragments which are near the transcription initiation site and form complexes that allow polymerase enzymes to bind to initiate transcription. Previous studies showed that methylated DNAs had ability to inhibit and prevent TFs from binding to DNA fragments. However, recent studies have found that there were TFs that could bind to methylated DNA fragments. The identification of these TFs is an important steppingstone to a better understanding of cellular gene expression mechanisms. However, as experimental methods are often time-consuming and labor-intensive, developing computational methods is essential. In this study, we propose two machine learning methods for two problems: (1) identifying TFs and (2) identifying TFs that prefer binding to methylated DNA targets (TFPMs). For the TF identification problem, the proposed method uses the position-specific scoring matrix for data representation and a deep convolutional neural network for modeling. This method achieved 90.56% sensitivity, 83.96% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.9596 on an independent test set. For the TFPM identification problem, we propose to use the reduced g-gap dipeptide composition for data representation and the support vector machine algorithm for modeling. This method achieved 82.61% sensitivity, 64.86% specificity, and an AUC of 0.8486 on another independent test set. These results are higher than those of other studies on the same problems.

BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting.

From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people's lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 [Formula: see text] and 0.012 MAPE at world level with 31-step forecast and up to 0.99 [Formula: see text] and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.

Function-Oriented Synthesis: Design, Synthesis, and Evaluation of Highly Simplified Bryostatin Analogues.

Using a function-oriented synthesis strategy, we designed, synthesized, and evaluated the simplest bryostatin 1 analogues reported to date, in which bryostatin's A- and B-rings are replaced by a glutarate linker. These analogues, one without and one with a C26-methyl group, exhibit remarkably different protein kinase C (PKC) isoform affinities. The former exhibited bryostatin-like binding to several PKC isoforms with Ki's < 5 nM, while the latter exhibited PKC affinities that were up to ∼180-fold less potent. The analogue with bryostatin-like PKC affinities also exhibited bryostatin-like PKC translocation kinetics in vitro, indicating rapid cell permeation and engagement of its PKC target. This study exemplifies the power of function-oriented synthesis in reducing structural complexity by activity-informed design, thus enhancing synthetic accessibility, while still maintaining function (biological activity), collectively providing new leads for addressing the growing list of therapeutic indications exhibited by PKC modulators.

Classification of adaptor proteins using recurrent neural networks and PSSM profiles.

BACKGROUND: Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem. RESULTS: Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively. CONCLUSIONS: This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.

iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.

BACKGROUND: Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and k-mers for data transformation and ensembles of convolutional neural networks for model construction, to identify enhancers and classify their strength. The benchmark dataset from Liu et al.'s study was used to develop and evaluate the ensemble models. A comparative analysis between iEnhancer-ECNN and existing state-of-the-art methods was done to fairly assess the model performance. RESULTS: Our experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews's correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively. CONCLUSIONS: iEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.

iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.

BACKGROUND: Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done. RESULTS: Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%. CONCLUSIONS: According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.

OBJECTIVE: Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: The proposed method was implemented by training various models into a logistic loss function using a stochastic gradient descent. We applied this model using public hospital record data provided by the Practice Fusion EHRs for the United States population. The dataset consists of de-identified electronic health records for 9948 patients, of which 1904 have been diagnosed with T2DM. Prediction of diabetes in 2012 was based on data obtained from previous years (2009-2011). The imbalance class of the model was handled by Synthetic Minority Oversampling Technique (SMOTE) for each cross-validation training fold to analyse the performance when synthetic examples for the minority class are created. We used SMOTE of 150 and 300 percent, in which 300 percent means that three new synthetic instances are created for each minority class instance. This results in the approximated diabetes:non-diabetes distributions in the training set of 1:2 and 1:1, respectively. RESULTS: Our final ensemble model not using SMOTE obtained an accuracy of 84.28%, area under the receiver operating characteristic curve (AUC) of 84.13%, sensitivity of 31.17% and specificity of 96.85%. Using SMOTE of 150 and 300 percent did not improve AUC (83.33% and 82.12%, respectively) but increased sensitivity (49.40% and 71.57%, respectively) with a moderate decrease in specificity (90.16% and 76.59%, respectively). DISCUSSION AND CONCLUSIONS: Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.

Results from the United States cohort of the HORIZON trial of a Schlemm canal microstent to reduce intraocular pressure in primary open-angle glaucoma.

PURPOSE: To assess the safety and effectiveness of Schlemm canal stenting for reducing intraocular pressure (IOP) in combination with cataract surgery in the United States cohort of the HORIZON study. SETTING: Twenty-six clinical sites in the U.S. DESIGN: Prospective clinical trial. METHODS: Eyes with mild to moderate primary open-angle glaucoma (POAG) on 1 to 4 medications, significant cataract, and an unmedicated diurnal IOP between 22 mm Hg and 34 mm Hg after medication washout were randomized 2:1 to receive the Hydrus microstent or no further treatment after successful cataract surgery. Patients were followed for 24 months. Medication washout and diurnal IOP measurements were repeated at 12 months and 24 months. RESULTS: Two hundred nineteen eyes were randomized to microstent implantation and 112 patients to phacoemulsification only. At 24 months, the diurnal IOP was reduced by 20.0% or more in a greater proportion of eyes in the microstent group (78.5% versus 54.5%; P < .001). The mean change in the number of medications was -1.2 ± 0.9 (SD) in the microstent group and -0.8 ± 1.1 in the phaco-only group (P < .001), and 78.5% of eyes and 39.2% of eyes, respectively, were medication free (difference 38.8%; P < .001). CONCLUSIONS: Implantation of a Schlemm canal microstent after phacoemulsification significantly reduced diurnal IOP and medication use compared with phacoemulsification only in patients with mild to moderately severe POAG. The combination procedure was equivalent to cataract surgery alone in terms of visual acuity outcomes and the rate of adverse ocular events.

iPseU-NCP: Identifying RNA pseudouridine sites using random forest and NCP-encoded features.

BACKGROUND: Pseudouridine modification is most commonly found among various kinds of RNA modification occurred in both prokaryotes and eukaryotes. This biochemical event has been proved to occur in multiple types of RNAs, including rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, gaining a holistic understanding of pseudouridine modification can contribute to the development of drug discovery and gene therapies. Although some laboratory techniques have come up with moderately good outcomes in pseudouridine identification, they are costly and required skilled work experience. We propose iPseU-NCP - an efficient computational framework to predict pseudouridine sites using the Random Forest (RF) algorithm combined with nucleotide chemical properties (NCP) generated from RNA sequences. The benchmark dataset collected from Chen et al. (2016) was used to develop iPseU-NCP and fairly compare its performances with other methods. RESULTS: Under the same experimental settings, comparing with three state-of-the-art methods including iPseU-CNN, PseUI, and iRNA-PseU, the Matthew's correlation coefficient (MCC) of our model increased by about 20.0%, 55.0%, and 109.0% when tested on the H. sapiens (H_200) dataset and by about 6.5%, 35.0%, and 150.0% when tested on the S. cerevisiae (S_200) dataset, respectively. This significant growth in MCC is very important since it ensures the stability and performance of our model. With those two independent test datasets, our model also presented higher accuracy with a success rate boosted by 7.0%, 13.0%, and 20.0% and 2.0%, 9.5%, and 25.0% when compared to iPseU-CNN, PseUI, and iRNA-PseU, respectively. For majority of other evaluation metrics, iPseU-NCP demonstrated superior performance as well. CONCLUSIONS: iPseU-NCP combining the RF and NPC-encoded features showed better performances than other existing state-of-the-art methods in the identification of pseudouridine sites. This also shows an optimistic view in addressing biological issues related to human diseases.

Combination of brinzolamide and brimonidine for glaucoma and ocular hypertension: critical appraisal and patient focus.

Glaucoma is one of the leading causes of blindness and is characterized by optic nerve damage that results in visual field loss. Elevated intraocular pressure (IOP) has been associated with glaucoma progression; thus, IOP-lowering medications are the standard of care for glaucoma. Guidelines suggest monotherapy with IOP-lowering agents such as ß-blockers (eg, timolol), prostaglandin analogs, carbonic anhydrase inhibitors (eg, brinzolamide), and α2-receptor agonists (eg, brimonidine). However, monotherapy may provide insufficient IOP reduction in some patients, thereby necessitating the use of multiple IOP-lowering medications. Multidrug regimens may be complex, may increase the risk of preservative-related ocular symptoms, and may potentially reduce overall drug exposure as a consequence of drug washout during closely timed sequential administrations; these difficulties may reduce overall drug efficacy and decrease patient persistence and adherence with multidrug treatment regimens. Fixed-combination medications that provide two IOP-lowering therapies within a single solution are available and may overcome some of these challenges. However, all currently available fixed combinations combine timolol with another IOP-lowering agent, indicating that additional fixed-combination alternatives would be beneficial. To meet this demand, a novel fixed combination of brinzolamide 1% and brimonidine 0.2% (BBFC) has recently been developed. In two randomized, double-masked, multinational clinical trials, BBFC had greater IOP-lowering efficacy than brinzolamide or brimonidine monotherapy after 3 months of treatment in patients with open-angle glaucoma or ocular hypertension. In both studies, the overall safety profile of BBFC was consistent with that of brinzolamide and brimonidine. Comparative studies with BBFC versus other IOP-lowering monotherapy and fixed-combination medications are not available, but the IOP reductions observed with BBFC are similar to or greater than those reported in the literature for other glaucoma treatments; thus, BBFC provides an additional fixed-combination therapeutic option for patients who require further efficacious IOP reduction and improved convenience and tolerability versus concomitant administration of two separate medications.

Effectiveness of intraocular pressure-lowering medication determined by washout.

IMPORTANCE While medication efficacy is well documented in clinical trials, less is known of medication effectiveness in real-world clinical settings. OBJECTIVE To assess the effectiveness of intraocular pressure (IOP)-lowering medications in patients with open-angle glaucoma. DESIGN, SETTING, AND PARTICIPANTS Prospective, multicenter, interventional cohort from the prerandomization phase of a randomized clinical trial at multiple ophthalmology clinics. A total of 603 patients (603 eyes) with primary open-angle glaucoma who were using up to 3 glaucoma medications were included. INTERVENTIONS One IOP measurement was made while the patient was using his or her usual medications to lower IOP (ON IOP). Eligible participants underwent washout of all IOP-lowering drops, and the diurnal IOP was measured 2 to 4 weeks later (OFF IOP). MAIN OUTCOMES AND MEASURES Difference between OFF IOP and ON IOP. The hypothesis was formulated after data collection. RESULTS The mean (SD) ON IOPs for participants using 0 (n = 102), 1 (n = 272), 2 (n = 147), or 3 (n = 82) medications were 24.2 (3.2), 17.5 (3.2), 17.2 (3.1), and 17.2 (3.1) mm Hg, respectively. Patients not using medication had a mean (SD) IOP decrease of 0.2 (2.8) mm Hg at the OFF visit. Patients using 1, 2, and 3 medications had mean (SD) IOP increases of 5.4 (3.0), 6.9 (3.3), and 9.0 (3.8) mm Hg, respectively, at the OFF visit. The percentages of patients with less than a 25% increase in IOP were 38%, 21%, and 13% for those using 1, 2, and 3 medications, respectively. CONCLUSIONS AND RELEVANCE Discontinuation of 1, 2, and 3 medications was associated with a clinically significant increase in IOP, although with smaller effects for the second and third medications compared with the first medication. A substantial proportion of patients showed only small changes in IOP after medication washout, suggesting either that they were not using the medication effectively or that the medication itself, although used properly, was not lowering the IOP. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01085357.

Six-month results from a Phase III randomized trial of fixed-combination brinzolamide 1% + brimonidine 0.2% versus brinzolamide or brimonidine monotherapy in glaucoma or ocular hypertension.

BACKGROUND: The objective of this study was to examine the safety and intraocular pressure (IOP)-lowering efficacy of a fixed combination of brinzolamide 1% + brimonidine 0.2% (BBFC) after six months of treatment in patients with open-angle glaucoma or ocular hypertension. METHODS: This was a randomized, multicenter, double-masked, three-month, three-arm contribution-of-elements study with a three-month safety extension. Patients were randomly assigned 1:1:1 to treatment with BBFC, brinzolamide 1%, or brimonidine 0.2% after a washout period. Patients dosed their study medications three times daily at 8 am, 3 pm, and 10 pm for six months. Patients returned for visits at two weeks, six weeks, three months, and six months. IOP measurements were used to assess efficacy. Safety assessments were adverse events, corrected distance visual acuity, slit-lamp biomicroscopy, pachymetry, perimetry, fundus parameters, and cardiac parameters. RESULTS: A total of 690 patients were randomized. Six-month mean IOP values were similar to those at three months, when the mean IOP in patients treated with BBFC was significantly lower than that of either monotherapy group. A total of 175 patients experienced at least one treatment-related adverse event (BBFC, 33.0%; brinzolamide, 18.8%; brimonidine, 24.7%), eight of which were severe, and five resulted in discontinuation. Seventy-seven patients discontinued participation due to treatment-related adverse events (BBFC, 17.2%; brinzolamide, 2.1%; brimonidine, 14.5%). There were 21 serious adverse events (n = 7 in each group), none of which was related to treatment. Resting mean pulse and blood pressure with BBFC were similar to those with brimonidine, demonstrating modest, clinically insignificant decreases. No new or increased risks were identified with use of BBFC relative to either monotherapy. CONCLUSION: This study showed that, after six months of treatment, the safety profile of BBFC was similar to that of its individual components and its IOP-lowering activity was similar to its efficacy at three months, when it was superior to both brinzolamide 1% alone and brimonidine 0.2% alone.

Early postoperative safety and surgical outcomes after implantation of a suprachoroidal micro-stent for the treatment of open-angle glaucoma concomitant with cataract surgery.

PURPOSE: To evaluate the safety of a new suprachoroidal device, the Cypass micro-stent, for the surgical treatment of open-angle glaucoma (OAG) implanted in conjunction with cataract surgery. SETTING: Multicenter clinical study. DESIGN: Prospective interventional case series. METHODS: This is an interim report of an ongoing safety study. Patients with OAG glaucoma (Shaffer grade 3 and 4) who were also candidates for cataract surgery in the affected eye had standard phacoemulsification followed by micro-stent implantation in the supraciliary space. Included were patients with uncontrolled (≥ 21 mm Hg, Cohort 1) or controlled (<21 mm Hg, Cohort 2) medicated intraocular pressure (IOP) at baseline. Glaucoma medications were discontinued at surgery and resumed at the discretion of each investigator. Measures included adverse events/complications and postoperative changes in IOP or medication. RESULTS: The mean baseline medicated IOP was 21.1 mm Hg ± 5.91 (SD); the mean number of IOP-lowering medications was 2.1 ± 1.1 (N = 184). There were no major events such as retinal or choroidal detachment or endophthalmitis. The most common complications were transient early hypotony (13.8%) and transient IOP increase (10.5%). Uncontrolled patients (n = 57) had a 37% IOP reduction (P<.001), with more than a 50% reduction in glaucoma medications at 6 months (P<.001). Intraocular pressure-controlled patients (n = 41) had a 71.4% reduction in glaucoma medications (P<.001). CONCLUSION: Initial clinical experience with the new micro-stent showed a low rate of surgical complications with concomitant decreases in IOP and/or glaucoma medications.

Phase 3 randomized 3-month trial with an ongoing 3-month safety extension of fixed-combination brinzolamide 1%/brimonidine 0.2%.

PURPOSE: This study compared the intraocular pressure (IOP)-lowering efficacy of fixed-combination brinzolamide 1%/brimonidine 0.2% (BBFC) with that of its component medications, brinzolamide and brimonidine, in patients with open-angle glaucoma or ocular hypertension. PATIENTS AND METHODS: In this phase 3, multicenter, double-masked, parallel-group, 3-month study with a 3-month safety extension, eligible patients were randomized 1:1:1 to treatment with BBFC, brinzolamide, or brimonidine thrice daily after a washout period, during which any IOP-lowering medications were discontinued. The primary objectives of this study were to determine whether the IOP-lowering efficacy of BBFC was superior to that of brinzolamide alone and, separately, of brimonidine alone. IOP was assessed at 8:00 AM, 10:00 AM, 3:00 PM, and 5:00 PM at 2 weeks, 6 weeks, and 3 months after study drug initiation. RESULTS: A total of 690 patients were enrolled in the study, and 615 completed the 3-month visit. Baseline mean IOP levels were similar among the 3 treatment groups at each of the 4 time points assessed. At the 3-month primary endpoint, mean IOP of the BBFC group was significantly lower than that of either the brinzolamide group or the brimonidine group (P≤0.005) across all time points. At the 2- and 6-week supportive endpoints, mean IOP of the BBFC group was significantly lower at all time points than the mean IOP of either the brinzolamide group (P≤0.01) or the brimonidine group (P<0.0001). A total of 143 patients experienced at least 1 treatment-related adverse event (AE; BBFC group, n=58, 26.2%; brinzolamide group, n=44, 18.8%; brimonidine group, n=41, 17.4%), the majority of which were ocular AEs. CONCLUSIONS: This study demonstrated that BBFC has significantly superior IOP-lowering activity compared with either brinzolamide 1% or brimonidine 0.2% in patients with open-angle glaucoma or ocular hypertension while providing a safety profile which is consistent with that of the individual components.