Alzheimer’s disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer’s disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer’s disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.

Medication Adherence Report Scale questionnaire (MARS-5) and the Beliefs about Medication Questionnaire-specific (BMQ-specific) are well known tools to assess adherence to medication and beliefs of chronic patients. However, validated Arabic versions of such questionnaires are lacking. We aim to validate the Arabic versions of BMQ-specific and MARS-5. In this study, a cross-sectional study was performed between November 2019 and March 2020. Participants were reached from secondary and tertiary care clinics in Jordan. Exploratory factor analysis (EFA) and Confirmatory Factor analysis (CFA) were conducted to validate the employed questionnaires on the tested sample. The internal consistency of the questionnaires was assessed by calculating Cronbach’s alpha, and Cronbach’s alpha if item is deleted. A total of 485 patients who met the inclusion criteria were recruited. The mean age of the participants was 57.14 years (22–82 years); and 39% of the participants were older than 65 years. The most common chronic diseases reported by participants were hypertension and diabetes mellitus, 35.7 and 32.2% respectively. EFA suggested two-factor model for BMQ-specific and one-factor model for MARS-5 which was confirmed by CFA analyses. The resulted Cronbach’s alphas of the questionnaires ranged from 0.89–0.93. Both analyses showed that the Arabic versions of both MARS-5 and BMQ-specific are valid and can be used for the suggested study population. Further validation-based research may enhance the transcultural adaptation of such questionnaires.

This study aimed to develop and evaluate thermoresponsive in situ microgels for the local ocular delivery of prednisolone (PRD) (PRD microgels) to improve drug bioavailability and prolong ocular drug residence time. Lipid nanosystems of PRD microemulsions (PRD-MEs) were prepared and evaluated at a drug concentration of 0.25–0.75%. PRD microgels were prepared by incorporating PRD-MEs into 10 and 12% Pluronic^® F127 (F127) or combinations of 12% F127 and 1–10% Kolliphor^®P188 (F68). PRD microgels were characterized for physicochemical, rheological, and mucoadhesive properties, eye irritation, and stability. Results showed that PRD-MEs were clear, miscible, thermodynamically stable, and spherical with droplet size (16.4 ± 2.2 nm), polydispersity index (0.24 ± 0.01), and zeta potential (−21.03 ± 1.24 mV). The PRD microgels were clear with pH (5.37–5.81), surface tension (30.96–38.90 mN/m), size, and zeta potential of mixed polymeric micelles (20.1–23.9 nm and −1.34 to −10.25 mV, respectively), phase transition temperature (25.3–36 °C), and gelation time (1.44–2.47 min). The FTIR spectra revealed chemical compatibility between PRD and microgel components. PRD microgels showed pseudoplastic flow, viscoelastic and mucoadhesive properties, absence of eye irritation, and drug content (99.3 to 106.3%) with a sustained drug release for 16–24 h. Microgels were physicochemically and rheologically stable for three to six months. Therefore, PRD microgels possess potential vehicles for local ocular delivery.

Introduction: Adherence to medications is very crucial for an optimized clinical outcome in the management of chronic diseases. Beliefs about medications and other factors can significantly affect adherence to chronic medications. The objective of the present research was to identify the associated factors of adherence to medication in Jordanian patients with chronic diseases utilizing a stepwise binary logistical regression model. Methods: A cross-sectional study was carried out between November 2018 and March 2020. The participants were reached from secondary and tertiary care setting clinics in Jordan. The recruited patients were asked to report their attitudes of adherence to medications and beliefs about medications via filling out the MARS-5 and BMQ-specific tools. Sociodemographic data were also collected from the recruited patients and included in the regression model. A stepwise binary logistical regression model was applied to identify the associated factors of adherence to chronic medications in the tested sample. Results: A total of 485 patients who met the inclusion criteria were recruited. The mean age of the participants was 57.14 (age ranged from 22 to 82 years). Around 39% of the participants were older than 65 years. Most of the patients were either hypertensive or diabetic (35.7% and 32.2%, respectively). The logistic regression model indicated that necessity beliefs are strongly associated with adherence (OR 4.22), while concerns beliefs, dosage frequency and having medical insurance were negatively associated with adherence (OR 0.73, 0.74 and 0.26, respectively), with a p-value ≤ 0.05. Conclusions: Both the MARS-5 and BMQ-specific questionnaires were applied successfully on the tested sample. Better attention should be paid to the logistic regression model variables that were associated with adherence in order to guarantee optimal treatment outcomes in the treatment of chronic diseases.

Despite the great technological and medical advances in fighting viral diseases, new therapies for most of them are still lacking, and existing antivirals suffer from major limitations regarding drug resistance and a limited spectrum of activity. In fact, most approved antivirals are directly acting antiviral (DAA) drugs, which interfere with viral proteins and confer great selectivity towards their viral targets but suffer from resistance and limited spectrum. Nowadays, host-targeted antivirals (HTAs) are on the rise, in the drug discovery and development pipelines, in academia and in the pharmaceutical industry. These drugs target host proteins involved in the virus life cycle and are considered promising alternatives to DAAs due to their broader spectrum and lower potential for resistance. Herein, we discuss an important class of HTAs that modulate signal transduction pathways by targeting host kinases. Kinases are considered key enzymes that control virus-host interactions. We also provide a synopsis of the antiviral drug discovery and development pipeline detailing antiviral kinase targets, drug types, therapeutic classes for repurposed drugs, and top developing organizations. Furthermore, we detail the drug design and repurposing considerations, as well as the limitations and challenges, for kinase-targeted antivirals, including the choice of the binding sites, physicochemical properties, and drug combinations.