Protocol deviations in inherited retinal disease (IRD) ophthalmology trials represent a multifaceted operational challenge threatening participant safety and primary endpoint data integrity (Getz, 2011; Cilley, 2025). These deviations span imaging acquisition, functional testing, investigational product handling, and visit scheduling. Unlike conventional pharmacological trials, IRD gene therapy involves irreversible surgical interventions where deviations cannot be recovered through repeat visits (Russell, 2017).

This article presents a comprehensive framework for classifying protocol deviation types in IRD trials and identifies strategic intervention points enabling proactive prevention through structured pre-visit preparation and real-time escalation protocols.

The Procedure Criticality Checklist (PCC) system applies risk-based monitoring principles (International Council for Harmonization, 2023) by severity and implementing targeted prevention mechanisms. The framework integrates evidence from surgical safety science (Haynes, 2009), quality-by-design methodology (TransCelerate Biopharma, 2023), IRD trial operations literature (Thompson, 2020).

The framework comprises: (1) a deviation taxonomy, (2) criticality-stratified pre-visit checklists, (3) tiered escalation pathways with predefined decision authorities, and (4) electronic integration mechanisms real-time monitoring.

Implementation is anticipated to reduce preventable deviations, improve data integrity, strengthen regulatory submissions, and accelerate approval timelines for ATMP ophthalmology programs.

This framework addresses a critical gap in IRD trial operations by providing standardized, operationalizable deviation prevention methodology suitable for adoption across national research networks (Igoe, 2024).

Objective: To evaluate the distribution of alleles and genotypes of the Lys198Asn (rs5370) polymorphism in the EDN1 gene and determine its association with the risk of various pathogenetic variants of congenital anomalies (CAs) in newborns. 

Materials and Methods: A molecular genetic study was conducted on 123 newborns with CAs (main group) and 110 healthy infants (control group) using PCR.  Statistical analysis included the Chi-squared test ($\chi^2$), Odds Ratio (OR), and 95% Confidence Interval (95% CI). 

Results: Accumulation of the mutant Asn allele was identified in the CA group (23.01% vs. 15.91% in controls), reaching a peak in subgroups with chromosomal pathology (26.47%) and folate-independent CAs (26.19%). The total proportion of Lys/Asn and Asn/Asn carrier genotypes in infants with CAs was 39.82% (vs. 28.18% in controls). A statistically significant association was established between the homozygous Asn/Asn genotype and the risk of CAs with chromosomal pathology (17.65% vs. 3.64% in controls; OR = 5.7; 95% CI: 1.34–24.09). For folate-independent CAs, the Asn/Asn genotype increased the risk by 2.8 times (OR = 2.8; 95% CI: 0,51–15,27). Receiver operating characteristic analysis revealed that the Lys/Asn genotype possesses balanced predictive value for primary screening (AUC = 0.55; OR = 1.56), while the Asn/Asn genotype demonstrates high specificity (SP = 0.96). 

Conclusions: The Lys198Asn polymorphism of the EDN1 gene is associated with an increased risk of congenital anomalies in newborns, exerting its strongest effect in cases with chromosomal aberrations and folate-independent CAs. The Asn/Asn genotype serves as a highly specific molecular biomarker for stratifying high reproductive risk groups.

Dry powder inhalers (DPIs) have become one of the most widely adopted pulmonary drug delivery systems because of their portability, propellant-free operation, formulation stability, and ability to deliver medications directly to the respiratory tract. Despite significant technological progress, conventional DPIs remain limited by their inability to objectively monitor patient adherence, inhalation technique, inspiratory flow variability, and real-time treatment outcomes. These limitations contribute to suboptimal disease control in chronic respiratory disorders such as asthma and chronic obstructive pulmonary disease (COPD). The rapid emergence of digital health technologies, including artificial intelligence (AI), Internet of Medical Things (IoMT), cloud computing, digital sensors, and mobile health platforms, has introduced new opportunities to transform conventional inhalation devices into intelligent therapeutic systems capable of continuous monitoring, personalized intervention, and data-driven clinical decision-making. This systematic review critically examines the current state of digital dry powder inhaler technologies by integrating evidence from recent developments in inhaler engineering, digital monitoring platforms, inhalation analytics, regulatory science, and patient-centered healthcare models.

The review synthesizes evidence from the selected literature to evaluate technological innovations in digital DPIs, including sensor-enabled adherence monitoring, inspiratory flow analysis, AI-assisted inhalation assessment, cloud-based healthcare integration, and remote disease management platforms. Particular attention is given to the evolving regulatory landscape governing digital inhalers, including device validation, software integration, cybersecurity, interoperability, clinical performance evaluation, and post-market surveillance. The review also explores challenges associated with technical standardization, patient usability, data privacy, economic feasibility, and clinical implementation. Comparative analysis demonstrates that digital integration significantly enhances treatment adherence, enables early identification of inhalation errors, supports personalized respiratory care, and improves healthcare efficiency while introducing new regulatory and implementation complexities. Recent investigations into digital inhaler platforms further demonstrate the growing importance of connected respiratory devices for long-term disease monitoring and adherence optimization (Aung et al., 2025).

The findings indicate that future DPI systems will increasingly evolve toward intelligent, connected, and predictive healthcare ecosystems supported by artificial intelligence, machine learning, remote patient monitoring, and real-time clinical analytics. Successful implementation, however, requires multidisciplinary collaboration among pharmaceutical scientists, device engineers, software developers, clinicians, regulatory authorities, and healthcare organizations. The review contributes a comprehensive synthesis of current developments while identifying future research priorities necessary for achieving safe, effective, and globally standardized digital inhaler technologies.