Amphiphilic Dendrimer Vectors for RNA Delivery: State-of-the-Art and Future Perspective.

Dendrimers, a special family of polymers, are particularly promising materials for various biomedical applications by virtue of their well-defined dendritic structure and cooperative multivalency. Specifically, in this Account, we present state-of-the-art amphiphilic dendrimers for nucleic acid delivery. Ribonucleic acid (RNA) molecules are fast becoming an important drug modality, particularly since the recent success of mRNA vaccines against COVID-19. Notably, RNA therapeutics offer the unique opportunity to treat diseases at the gene level and address "undruggable" targets. However, RNA therapeutics are not stable and have poor bioavailability, imposing the need for their protection and safe delivery by vectors to the sites-of-action to allow the desired therapeutic effects. Currently, the two most advanced nonviral vectors are based on lipids and polymers, with lipid vectors primarily exploiting the membrane-fusion mechanism and polymer vectors mainly endocytosis-mediated delivery. Notably, only lipid vectors have been advanced through to their clinical use in the delivery of, for example, the first siRNA drug and the first mRNA vaccine. The success of lipid vectors for RNA delivery has motivated research for further innovative materials as delivery vectors. Specifically, we have pioneered lipid/dendrimer conjugates, referred to as amphiphilic dendrimers, for siRNA delivery with the view to harnessing the delivery advantages of both lipid and polymer vectors while enjoying the unique structural features of dendrimers. These amphiphilic dendrimer vectors are lipid/dendrimer hybrids and are thus able to mimic lipid vectors and exploit membrane-fusion-mediated delivery, while simultaneously retaining the multivalent properties of polymer vectors that allow endocytosis-based delivery. In addition, they have precisely controllable and stable nanosized chemical structures and offer nanotechnology-based delivery. Effective amphiphilic dendrimer vectors share two important elements: chemical hydrophilic entities to bind RNA and RNA complex-stabilizing hydrophobicity. These two combined features allow the encapsulation of RNA within a stable complex before its release into the cytosol following endocytosis. This hydrophilic/hydrophobic balance permitted by the structural features of amphiphilic dendrimers plays a determining role in RNA delivery success. In this Account, we provide a conceptual overview of this exciting field with the latest breakthroughs and key advances in the design of amphiphilic dendrimers for the delivery of siRNA and mRNA. Specifically, we start with a short introduction to siRNA- and mRNA-based therapeutics and their delivery challenges. We then outline the pioneering and representative studies on amphiphilic dendrimer vectors to highlight their historical development and promising features that offer to facilitate the once challenging RNA delivery. We conclude by offering perspectives for the future of amphiphilic dendrimer vectors for nucleic acid delivery in general.

Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease (HERMES): a Cluster Randomised Superiority Trial with a linked Diagnostic Accuracy Study-HERMES study report 1-study protocol.

INTRODUCTION: Recent years have witnessed an upsurge of demand in eye care services in the UK. With a large proportion of patients referred to Hospital Eye Services (HES) for diagnostics and disease management, the referral process results in unnecessary referrals from erroneous diagnoses and delays in access to appropriate treatment. A potential solution is a teleophthalmology digital referral pathway linking community optometry and HES. METHODS AND ANALYSIS: The HERMES study (Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease: a cluster randomised superiority trial with a linked diagnostic accuracy study) is a cluster randomised clinical trial for evaluating the effectiveness of a teleophthalmology referral pathway between community optometry and HES for retinal diseases. Nested within HERMES is a diagnostic accuracy study, which assesses the accuracy of an artificial intelligence (AI) decision support system (DSS) for automated diagnosis and referral recommendation. A postimplementation, observational substudy, a within-trial economic evaluation and discrete choice experiment will assess the feasibility of implementation of both digital technologies within a real-life setting. Patients with a suspicion of retinal disease, undergoing eye examination and optical coherence tomography (OCT) scans, will be recruited across 24 optometry practices in the UK. Optometry practices will be randomised to standard care or teleophthalmology. The primary outcome is the proportion of false-positive referrals (unnecessary HES visits) in the current referral pathway compared with the teleophthalmology referral pathway. OCT scans will be interpreted by the AI DSS, which provides a diagnosis and referral decision and the primary outcome for the AI diagnostic study is diagnostic accuracy of the referral decision made by the Moorfields-DeepMind AI system. Secondary outcomes relate to inappropriate referral rate, cost-effectiveness analyses and human-computer interaction (HCI) analyses. ETHICS AND DISSEMINATION: Ethical approval was obtained from the London-Bromley Research Ethics Committee (REC 20/LO/1299). Findings will be reported through academic journals in ophthalmology, health services research and HCI. TRIAL REGISTRATION NUMBER: ISRCTN18106677 (protocol V.1.1).

Machine Learning for Health: Algorithm Auditing & Quality Control.

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.