As one of the leading causes of blindness, age-related macular degeneration (AMD) has remained at the epicenter of clinical research in ophthalmology. During the past decade, focus of researchers has ranged from understanding the role of vascular endothelial growth factor (VEGF) in the angiogenic cascades to developing new therapies for retinal vascular diseases. Anti-VEGF agents such as ranibizumab and aflibercept are becoming increasingly well-established therapies and have replaced earlier approaches such as laser photocoagulation or photodynamic therapy. Many other new therapeutic agents, which are in the early phase clinical trials, have shown promising results. The purpose of this paper is to briefly review the available treatment modalities for neovascular AMD and then focus on promising new therapies that are currently in various stages of development.

Chronic inflammation plays an important role in the pathogenesis of ocular diseases such as diabetic retinopathy, uveitis and age-related macular degeneration. Activation and proliferation of naïve T cells may result in pathological changes responsible for significant visual morbidity. Sirolimus (earlier termed rapamycin) is a novel drug that inhibits cellular kinases and, thereby, inhibits T-cell proliferation. Preclinical studies in experimental models have shown promising results with the use of this pharmacological agent in various ocular conditions. Subsequently, early phase I/II studies have provided encouraging safety and efficacy data. This chapter focuses on the mechanisms of action, preclinical study results and data from human clinical trials of sirolimus in human eye diseases. Key highlights from ongoing phase III clinical trials are also provided. Sirolimus, thus, appears to be an important addition to the armamentarium of steroid-sparing therapeutic agents that act on various steps in the inflammatory pathway.

Purpose To compare 2.0 mg ranibizumab (RBZ) injections with 0.5 mg RBZ for eyes with center-involved diabetic macular edema (DME) and a central subfield thickness (CFT) of ≥ 250 μm on time-domain optical coherence tomography. Design Randomized, controlled, multicenter clinical trial. Methods Eligible eyes were randomized in a 1:1 ratio to 0.5 mg (n = 77) or 2.0 mg (n = 75) RBZ. Study eyes received 6-monthly injections. Main outcome measures The primary outcome measure was the mean change in best corrected visual acuity (BCVA) at month 6. Secondary outcomes included the incidence and severity of systemic and ocular adverse events and the mean change in CFT from baseline. Results In all, 152 eyes (152 patients) were randomized in the study. At month 6, the mean improvement from baseline BCVA was +9.43 letters in the 0.5 mg RBZ group and +7.01 letters in the 2.0 mg RBZ group (P = 0.161). At month 6, one death occurred in the 0.5 mg RBZ group and three deaths in the 2.0 mg RBZ group, all due to myocardial infarction in subjects with a prior history of heart disease. Mean CFT was reduced by 168.58 μm in the 0.5 mg RBZ group and by 159.70 μm in the 2.0 mg RBZ group (P = 0.708). Conclusions There was no statistically significant difference in the mean number of letters gained between the 0.5 and 2.0 mg RBZ groups through month 6. In this DME study population, high-dose RBZ does not appear to provide additional benefit over 0.5 mg RBZ.

Aspiration during any kind of injection is meant to ensure that the needle tip is at the desired location during this blind procedure. While aspiration appears to be a simple procedure, it has generated a lot of controversy concerning the perceived benefits and indications. Advocates and opponents of aspiration both make logically sound claims. However, due to scarcity of available data, there is no evidence that this procedure is truly beneficial or unwarranted. Keeping in view the huge number of injections given worldwide, it is important that we draw attention to key questions regarding aspiration that, up till now, remain unanswered. In this review, we have attempted to gather and present literature on aspiration both from published and non-published sources in order to provide not only an exhaustive review of the subject, but also a starting point for further studies on more specific areas requiring clarification. A literature review was conducted using the US National Institute of Health’s PubMed service (including Medline), Google Scholar and Scopus. Guidelines provided by the World Health Organization, Safe Injection Global Network, International Council of Nursing, Center for Disease Control, US Federal Drug Agency, UK National Health Services, British Medical Association, Europe Nursing and Midwifery Council, Public Health Agency Canada, Pakistan Medical Association and International Organization of Standardization recommendations 7886 parts 1-4 for sterile hypodermics were reviewed for relevant information. In addition, curricula of several medical/nursing schools from India, Nigeria and Pakistan, the US pharmacopeia Data from the WHO Program for International Drug Monitoring network in regard to adverse events as a result of not aspirating prior to injection delivery were reviewed. Curricula of selected major medical/nursing schools in India, Nigeria and Pakistan, national therapeutic formularies, product inserts of most commonly used drugs and other possible sources of information regarding aspiration and injections were consulted as well.

Accurate and robust diagnosis of retinal diseases using OCT imaging is considered an essential part for clinical utility. We propose a deep learning based, a fully automated diagnosis system for detecting retinal disorders namely, Drusen macular degeneration (DMD) and diabetic macular edema (DME) using optical coherence tomography (OCT) Images. If it is not diagnosed and treated, these degenerative abnormalities may result in moderate to severe vision loss. Early detection of these diseases reduces the risk of further complications and expedites the treatment process. We propose a deep convolutional neural network (CNN) framework for the diagnosis and classification into Normal, DMD and DME effectively. First, despeckling of the input OCT images is performed using the Kuan filter to remove inherent speckle noise. Further, the CNN network is tuned using hyperparameter optimization techniques. Additionally, K-fold validation is performed to ensure complete usage of the dataset. We evaluate the proposed model with number of performance metrics using Mendeley database consisting of labelled OCT images. The resulting classification accuracy of the proposed model is 95.7%. Further, an authoritative study is performed between the pre-trained models and proposed framework using the acquired performance metrics to demonstrate the efficacy of our model.