Twins' Macular Pigment Optical Density Assessment and Relation with SCARB1 Gene Polymorphism.

THE AIM OF THE STUDY: to assess the influence of genetic and environmental factors using twin studies and evaluate the associations of SCARB1 gene variants (rs11057841) with AMD and MPOD. MATERIAL AND METHODS: a total of 108 healthy twins (56 MZ and 52 DZ twins) were tested in this study. The MPOD was measured using the one-wavelength reflectometry method. Fundus reflectance (Visucam 500, reflectance of a single 460 nm wavelength) was used to measure the MPOD levels, MPOD parameters including max and mean optical density (OD), and area and volume. Real-time polymerase chain reaction was used to detect single nucleotide polymorphisms. RESULTS: we detected a positive correlation of MPOD in the right and left eyes in MZ twin pairs (r = 0.830 and r = 0.860, respectively) (p < 0.0001) and a negative correlation of MPOD in the right and left eyes in DZ twin pairs (r = 0.314 and r = 0.408, respectively) (p < 0.05). The study was able to identify statistically significant differences in mean MPOD values in the right and left eyes between subjects with a wild-type CC genotype and a CT genotype with a risk allele. A decrease in the mean MPOD value was observed in group II with a CT genotype (0.110 d.u.) compared with the CC genotype (0.117 d.u.) in the right eye (p = 0.037) and in the left eye with a CT genotype (0.109 d.u.) compared with a CC genotype in the subjects (0.114 d.u.) (p = 0.038). In the right eye, in group II (0.101-0.128 d.u.), those with a CT genotype (n = 6) with one risk allele had a statistically significantly lower (0.110 d.u.) mean average MPOD value compared with those with a wild-type CC genotype (n = 25) (0.117 d.u.) (p = 0.037). CONCLUSION: this twin study showed a strong heritability of the retina pigment, which was 86% prevalent in Lithuania. Individuals with a CT genotype of the SCARB1 rs11057841 with a risk allele had statistically significantly lower mean MPOD values in both eyes compared to subjects with a wild-type CC genotype.

Associations of Tumor Necrosis Factor-Alpha Gene Polymorphisms (TNF)-α TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), TNF-238A/G (rs361525), and TNF-Alpha Serum Concentration with Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a neurodegenerative disease leading to irreversible central vision loss among the elderly in developed countries. While the disease accounts for 9% of all cases of vision loss, the prevalence of AMD is likely to increase due to the exponential aging of the population. Due to this reason, our study aimed to determine the associations of tumor necrosis factor-alpha (TNF-α) gene single-nucleotide polymorphisms (SNPs) TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), TNF-238A/G (rs361525), and TNF-α serum concentration with age-related macular degeneration. Analysis of TNF-α rs1800630, rs1800629, and rs361525 polymorphisms showed that the TNF-α rs1800630 A allele was statistically significantly more frequent in the exudative AMD group compared to the control group (p = 0.029). Additionally, the TNF-α rs1800630 A allele was more frequent in females with exudative AMD than in the control group of healthy females (p = 0.027). The TNF-α rs1800630 A allele was more frequent in females with exudative AMD than in females with early AMD (p = 0.014). TNF-α rs1800630, rs1800629, and rs361525 haplotype A-A-G were associated with decreased odds of exudative AMD (p < 0.0001), and haplotype A-G-G was associated with 24-fold increased exudative AMD occurrence (p < 0.0001). TNF-α protein levels were lower in subjects with exudative AMD compared to the control group (p < 0.001). The study showed significant associations between inflammatory cytokine TNF-α single-nucleotide polymorphisms and serum level with AMD pathogenesis. Analysis of TNF-α genotypes and serum concentration may be helpful for the AMD diagnosis.

SIRT1: Genetic Variants and Serum Levels in Age-Related Macular Degeneration.

Background: The aim of this paper was to determine the frequency of SIRT1 rs3818292, rs3758391, rs7895833 single nucleotide polymorphism genotypes and SIRT1 serum levels associated with age-related macular degeneration (AMD) in the Lithuanian population. Methods: Genotyping of SIRT1 rs3818292, rs3758391 and rs7895833 was performed using RT-PCR. SIRT1 serum level was determined using the ELISA method. Results: We found that rs3818292 and rs7895833 were associated with an increased risk of developing exudative AMD. Additional sex-differentiated analysis revealed only rs7895833 was associated with an increased risk of developing exudative AMD in women after strict Bonferroni correction. The analysis also revealed that individuals carrying rs3818292, rs3758391 and rs7895833 haplotype G-T-G are associated with increased odds of exudative AMD. Still, the rare haplotypes were associated with the decreased odds of exudative AMD. After performing an analysis of serum SIRT1 levels and SIRT1 genetic variant, we found that carriers of the SIRT1 rs3818292 minor allele G had higher serum SIRT1 levels than the AA genotype. In addition, individuals carrying at least one SIRT1 rs3758391 T allele also had elevated serum SIRT1 levels compared with individuals with the wild-type CC genotype. Conclusions: Our study showed that the SIRT1 polymorphisms rs3818292 and rs7895833 and rs3818292-rs3758391-rs7895833 haplotype G-T-G could be associated with the development of exudative AMD. Also, two SNPs (rs3818292 and rs3758391) are associated with elevated SIRT1 levels.

ABCA1 rs1883025 and CYP4F2 rs2108622 Gene Polymorphism Association with Age-Related Macular Degeneration and Anti-VEGF Treatment.

Background and Objectives: The age-related macular degeneration (AMD) pathophysiology is multifactorial, as it consists of interactions between aging, genetic, and environmental factors. We aimed to determine a relationship between AMD and the genes controlling lipid metabolism, and to assess its association with treatment results. The purpose was to find the ABCA1 rs1883025 and CYP4F2 rs2108622 gene polymorphisms in patients with exudative AMD (eAMD) treated with anti-VEGF. Materials and Methods: The study enroled 104 patients with eAMD and 201 healthy persons in a control group. The genotyping of rs1883025 and rs2108622 was performed using the RT-PCR method. The best-corrected visual acuity (BCVA) and central retinal thickness (CRT) were measured before anti-VEGF therapy, then at three and six months during the therapy, using optical coherence tomography (OCT). The patients were grouped to responders and non-responders according to the changes in BCVA and CRT. Results: The T allele at rs1883025 was more frequent in non-responder eAMD patients compared to responder eAMD patients (41.7% vs. 21.1%; p = 0.009). The analysis of rs2108622 gene polymorphism did not reveal any differences in the distribution of C/C, C/T, and T/T genotypes between the eAMD group and the control group (56.35%, 39.78%, and 3.87% in the eAMD group and 53.33%, 39.05% and 7.62% in the control group, respectively, p = 0.286). The comparison of CRT and BCVA between the rs2108622 genotypes revealed statistically significant differences: CRT was thicker for the CC carriers than for those with CT and TT genotypes (p = 0.030). Conclusion: The rs1883025 T allele was found to play a more significant role in non-responder eAMD patients compared to responder eAMD patients. The rs2108622 genotypes revealed statistically significant differences: CRT was thicker for the CC carriers than for those with CT and TT genotypes.

The Role of SNPs in IL1RL1 and IL1RAP Genes in Age-related Macular Degeneration Development and Treatment Efficacy.

BACKGROUND: Age-related macular degeneration (AMD) affects the central part of the retina and causes blindness. In developed countries, AMD occurs in people over 50 years old. Important factors for AMD pathogenesis are an immune response, inflammation, and genetic factors. This study aimed to determine the impact of IL1RL1 rs1041973 and IL1RAP rs4624606 single nucleotide polymorphisms (SNPs) on the occurrence of AMD and the outcome of treatment with aflibercept and bevacizumab. PATIENTS AND METHODS: 563 patients with AMD and 281 healthy candidates were evaluated. Patients with exudative AMD were treated with intravitreal bevacizumab and aflibercept and, after 6 months based on the changes in best-corrected visual acuity and central macular thickness, were classified as 'responders' or 'poor-responders'. Genotyping of IL1RL1 rs1041973 and IL1RAP rs4624606 was accomplished using real-time PCR. Age was compared using the Mann-Whitney U-test. Categorical data (gender, genotype, and allele distributions) compared between groups using the χ2 test or the Fisher's exact test. Associations of gene polymorphisms were calculated using logistic regression analysis with adjustment for age in exudative and atrophic AMD analysis. An adjusted significance threshold for multiple comparisons α=0.025 was applied. RESULTS: Statistically significant differences in the distribution of IL1RAP rs4624606 genotypes (TT, TA and AA) were found between males with atrophic AMD and controls: 50%, 42.9% and 7.1% vs. 69.7%, 30.3% and 0%, respectively, p=0.015. Moreover, we found that 'responders' had a significantly better best-corrected visual acuity than 'poor-responders' before treatment (p=0.032). The central macular thickness was significantly lower in exudative AMD patients with IL1RL1 rs1041973 AA genotype than in wild type and heterozygous (CC+CA) genotype carriers before treatment (p=0.017). CONCLUSION: IL1RAP rs4624606 may be associated with atrophic AMD in males while IL1RL1 rs1041973 may play a protective role against macular thickening in exudative AMD patients.

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manually by pharmacovigilance experts. The dramatic increase in the volume of safety reports necessitates exploration of scalable solutions that also meet reporting timeline requirements. OBJECTIVE: The aim of this study was to develop an augmented intelligence methodology for automatically identifying adverse event seriousness in spontaneous, solicited, and medical literature safety reports. Deep learning models were evaluated for accuracy and/or the F1 score against a ground truth labeled by pharmacovigilance experts. METHODS: Using a stratified random sample of safety reports received by Celgene, we developed three neural networks for addressing identification of adverse event seriousness: (1) a binary adverse-event level seriousness classifier; (2) a classifier for determining seriousness categorization at the adverse-event level; and (3) an annotator for identifying seriousness criteria terms to provide supporting evidence at the document level. RESULTS: The seriousness classifier achieved an accuracy of 83.0% in post-marketing reports, 92.9% in solicited reports, and 86.3% in medical literature reports. F1 scores for seriousness categorization were 77.7 for death, 78.9 for hospitalization, and 75.5 for important medical events. The seriousness annotator achieved an F1 score of 89.9 in solicited reports, and 75.2 in medical literature reports. CONCLUSIONS: The results of this study indicate that a neural network approach can provide an accurate and scalable solution for potentially augmenting pharmacovigilance practitioner determination of adverse event seriousness in spontaneous, solicited, and medical literature reports.

Artificial Intelligence and the Future of the Drug Safety Professional.

The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from individual case safety reports (ICSRs). To cope with this increase, more healthcare and qualified professionals are required to capture and evaluate the data. To address the evolving landscape, it will be necessary to embrace assistive technologies such as artificial intelligence (AI) at scale. AI in the field of pharmacovigilance will possibly result in the transformation of the drug safety (DS) professional's daily work life and their career development. Celgene's Global Drug Safety and Risk Management (GDSRM) function has established a series of work activities to drive innovation across the pharmacovigilance value chain (Celgene Chrysalis Fact Sheet. https://www.celgene.com/newsroom/media-library/chrysalis-fact-sheet/, 2018). The development of AI in pharmacovigilance raises questions about the possible changes in DS professionals' lives, who may find themselves curious about their future roles in a workplace assisted by AI. We discuss the current state of pharmacovigilance and the DS professional, AI in pharmacovigilance and the potential skillsets a DS professional may require when working with AI. We also describe the results of research conducted at Celgene GDSRM. The objective of the research was to understand the thoughts of pharmacovigilance professionals about their jobs. These results are provided in the form of aggregated responses to interview questions based on a 12-part questionnaire [see the Electronic Supplementary Material (ESM)]. A sample of six DS professionals representing various areas of pharmacovigilance operations were asked a range of questions about their backgrounds, current roles and future expectations. The DS professionals interviewed were, overall, enthusiastic about their job roles potentially changing with AI enhancements. Interviewees suggested that AI would allow for pharmacovigilance resources, time, and skills to shift the work from a volume-based to a value-based focus. The results suggest that pharmacovigilance professionals wish to use their qualifications, skillsets and experience in work that provides more value for their efforts. Machine learning algorithms have the potential to enhance DS professionals' decision-making processes and support more efficient and accurate case processing.

Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is estimated that more than 90% of AEs go unreported. In this landscape, embracing assistive technologies at scale becomes necessary to obtain a higher yield of AEs, to maintain compliance, and transform the PV professional work life. AIM: The aim of this study was to identify areas across the PV value chain that can be augmented by cognitive service solutions using the methodologies of contextual analysis and cognitive load theory. It will also provide a framework of how to validate these PV cognitive services leveraging the acceptable quality limit approach. METHODS: The data used to train the cognitive service were an annotated corpus consisting of 20,000 ICSRS from which we developed a framework to identify and validate 40 cognitive services ranging from information extraction to complex decision making. This framework addresses the following shortcomings: (1) needing subject-matter expertise (SME) to match the artificial intelligence (AI) model predictions to the gold standard, commonly referred to as 'ground truth' in the AI space, (2) ground truth inconsistencies, (3) automated validation of prediction missing context, and (4) auto-labeling causing inaccurate test accuracy. The method consists of (1) conducting contextual analysis, (2) assessing human cognitive workload, (3) determining decision points for applying artificial intelligence (AI), (4) defining the scope of the data, or annotated corpus required for training and validation of the cognitive services, (5) identifying and standardizing PV knowledge elements, (6) developing cognitive services, and (7) reviewing and validating cognitive services. RESULTS: By applying the framework, we (1) identified 51 decision points as candidates for AI use, (2) standardized the process to make PV knowledge explicit, (3) embedded SMEs in the process to preserve PV knowledge and context, (4) standardized acceptability by using established quality inspection principles, and (5) validated a total of 126 cognitive services. CONCLUSION: The value of using AI methodologies in PV is compelling; however, as PV is highly regulated, acceptability will require assurances of quality, consistency, and standardization. We are proposing a foundational framework that the industry can use to identify and validate services to better support the gathering of quality data and to better serve the PV professional.

Changes in subfoveal choroidal thickness after uncomplicated cataract surgery.

BACKGROUND: Recent investigations show that phacoemulsification causing an inflammatory insult to the eye has an effect not only on retina but on the choroid as well. The purpose of this study was to evaluate the subfoveal choroidal thickness (SFCT) after uneventful phacoemulsification using swept-source optical coherence tomography (SS-OCT). METHODS: This prospective study included 30 eyes of 23 patients with senile cataract undergoing uncomplicated phacoemulsification and intraocular lens implantation. SFCT and foveal retinal thickness (FRT) measurements were made at the same time, 1-2 PM preoperatively (P), 1 month (M1) and 3 months (M3) postoperatively using 1050 nm DRI Triton SS-OCT (Topcon, Tokyo, Japan). Postoperative changes in the SFCT, FRT and correlation of SFCT change with axial length, age, baseline intraocular pressure (IOP), IOP change were assessed. RESULTS: The mean SFCT increased statistically significantly 3 months after surgery in all sectors except the superior inner region. Of the factors affecting the SFCT, the change in the SFCT (M3/P), correlation with age and baseline IOP in almost all sectors was observed. The mean FRT increased significantly after the surgery in all sectors. CONCLUSIONS: Insignificant subclinical increase in SFCT was observed 1 month after the cataract surgery. Significant increment in SFCT was detected 3 months postoperatively, which was correlated with surgery-induced IOP and ocular perfusion pressure change in the short term.

Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing.

INTRODUCTION: Regulations are increasing the scope of activities that fall under the remit of drug safety. Currently, individual case safety report (ICSR) collection and collation is done manually, requiring pharmacovigilance professionals to perform many transactional activities before data are available for assessment and aggregated analyses. For a biopharmaceutical company to meet its responsibilities to patients and regulatory bodies regarding the safe use and distribution of its products, improved business processes must be implemented to drive the industry forward in the best interest of patients globally. Augmented intelligent capabilities have already demonstrated success in capturing adverse events from diverse data sources. It has potential to provide a scalable solution for handling the ever-increasing ICSR volumes experienced within the industry by supporting pharmacovigilance professionals' decision-making. OBJECTIVE: The aim of this study was to train and evaluate a consortium of cognitive services to identify key characteristics of spontaneous ICSRs satisfying an acceptable level of accuracy determined by considering business requirements and effective use in a real-world setting. The results of this study will serve as supporting evidence for or against implementing augmented intelligence in case processing to increase operational efficiency and data quality consistency. METHODS: A consortium of ten cognitive services to augment aspects of ICSR processing were identified and trained through deep-learning approaches. The input data for model training were 20,000 ICSRs received by Celgene drug safety over a 2-year period. The data were manually made machine-readable through the process of transcription, which converts images into text. The machine-readable documents were manually annotated for pharmacovigilance data elements to facilitate the training and testing of the cognitive services. Once trained by cognitive developers, the cognitive services' output was reviewed by pharmacovigilance subject-matter experts against the accepted ground-truth for correctness and completeness. To be considered adequately trained and functional, each cognitive service was required to reach a threshold of F1 or accuracy score ≥ 75%. RESULTS: All ten cognitive services under development have reached an evaluative score ≥ 75% for spontaneous ICSRs. CONCLUSION: All cognitive services under development have achieved the minimum evaluative threshold to be considered adequately trained, demonstrating how machine-learning and natural language processing techniques together provide accurate outputs that may augment pharmacovigilance professionals' processing of spontaneous ICSRs quickly and accurately. The intention of augmented intelligence is not to replace the pharmacovigilance professional, but rather support them in their consistent decision-making so that they may better handle the overwhelming amount of data otherwise manually curated and monitored for ongoing drug surveillance requirements. Through this supported decision-making, pharmacovigilance professionals may have more time to apply their knowledge in assessing the case rather than spending it performing transactional tasks to simply capture the pertinent data within a safety database. By capturing data consistently and efficiently, we begin to build a corpus of data upon which analyses may be conducted and insights gleaned. Cognitive services may be key to an organization's transformation to more proactive decision-making needed to meet regulatory requirements and enhance patient safety.