Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi‐disciplinary approach can promote data‐driven decision‐making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation

Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.// METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.// RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.// DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.</p

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence.

This is the final version. Available from Wiley via the DOI in this record. BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: The DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com

Incidence of cancer and overall risk of mortality in individuals treated with raltegravir-based and non-raltegravir-based combination antiretroviral therapy regimens

Objectives: There are currently few data on the long-term risk of cancer and death in individuals taking raltegravir (RAL). The aim of this analysis was to evaluate whether there is evidence for an association. Methods: The EuroSIDA cohort was divided into three groups: those starting RAL-based combination antiretroviral therapy (cART) on or after 21 December 2007 (RAL); a historical cohort (HIST) of individuals adding a new antiretroviral (ARV) drug (not RAL) to their cART between 1 January 2005 and 20 December 2007, and a concurrent cohort (CONC) of individuals adding a new ARV drug (not RAL) to their cART on or after 21 December 2007. Baseline characteristics were compared using logistic regression. The incidences of newly diagnosed malignancies and death were compared using Poisson regression. Results: The RAL cohort included 1470 individuals [with 4058 person-years of follow-up (PYFU)] compared with 3787 (4472 PYFU) and 4467 (10 691 PYFU) in the HIST and CONC cohorts, respectively. The prevalence of non-AIDS-related malignancies prior to baseline tended to be higher in the RAL cohort vs. the HIST cohort [adjusted odds ratio (aOR) 1.31; 95% confidence interval (CI) 0.95–1.80] and vs. the CONC cohort (aOR 1.89; 95% CI 1.37–2.61). In intention-to-treat (ITT) analysis (events: RAL, 50; HIST, 45; CONC, 127), the incidence of all new malignancies was 1.11 (95% CI 0.84–1.46) per 100 PYFU in the RAL cohort vs. 1.20 (95% CI 0.90–1.61) and 0.83 (95% CI 0.70–0.99) in the HIST and CONC cohorts, respectively. After adjustment, there was no evidence for a difference in the risk of malignancies [adjusted rate ratio (RR) 0.73; 95% CI 0.47–1.14 for RALvs. HIST; RR 0.95; 95% CI 0.65–1.39 for RALvs. CONC] or mortality (adjusted RR 0.87; 95% CI 0.53–1.43 for RALvs. HIST; RR 1.14; 95% CI 0.76–1.72 for RALvs. CONC). Conclusions: We found no evidence for an oncogenic risk or poorer survival associated with using RAL compared with control groups.Peer reviewe

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Erdheim-Chester disease: a comprehensive review from the ophthalmologic perspective

Erdheim–Chester disease (ECD) is a rare clonal histiocytic neoplasm with less than 1200 documented cases to date. The disease is life-threatening and difficult to recognize, although increasing awareness as well as the integration of clinical, imaging, pathology information, and genetic studies have led to a recent exponential increase in new reported cases. ECD affects multiple organs and systems, including skeletal, neurologic, and cardiovascular. Pulmonary, retroperitoneal, and cutaneous lesions have also been reported in various combinations. Until the discovery that more than half of ECD patients harbor the BRAF-V600E mutation or other mutations in the mitogen-activated protein kinase (MAPK) and RAS pathways, Interferon-a was the first-line treatment. Nowadays BRAF and MEK-inhibitors targeted therapies are the mainstay of treatment. Ophthalmologic involvement occurs in 25% −30% of ECD cases, usually in the form of orbital involvement presenting with exophthalmos and ophthalmoplegia. Other ophthalmologic manifestations include palpebral xanthelasmas, anterior uveitis and vitritis, optic disk edema, choroidal infiltration, recurrent serous retinal detachment, retinal drusen–like deposits and retinal pigment epithelial changes. ECD patients can also present with ocular symptoms as a result of adverse effects of the treatment regimens. In some cases with smoldering or protean symptoms, the emergence of eye manifestations triggered the diagnosis. Ophthalmologists have to be aware of the disease, recognize the constellation of ECD symptoms, and contribute to the diagnosis, treatment, and follow-up of ECD patients. © 2021 Elsevier Inc

Arterial stiffness and orthostatic blood pressure changes in untreated and treated hypertensive subjects

Carotid-femoral pulse wave velocity (PWV), an integrated marker of segmental aortic stiffness, was recently proposed as one of the underlying mechanisms inducing orthostatic hypotension in the elderly with marked arterial rigidity. We examined the relationship between PWV (Complior; Colson, Paris, France) and orthostatic blood pressure (BP) changes, measured repeatedly, over a wide range of age and arterial stiffness. Sixty-nine hypertensive subjects (age, 37 to 76 years; 39 untreated and 30 treated) were studied. BP, in both sitting and erect position, was measured at two occasions a few weeks apart, and in between PWV was assessed by means of pulse wave analysis. In untreated hypertensive subjects, the orthostatic alterations in systolic, but not in diastolic blood pressure (DBP), were inversely related to PWV, independently from age, gender, mean BP, and diabetes mellitus. The greater the aortic stiffness the larger was the systolic blood pressure (SBP) decrease during upraises. On the contrary, no such association was found between PWV and orthostatic changes of BP in treated hypertensive subjects. These results suggest the presence of a pathophysiological association between arterial stiffening and BP postural changes. Antihypertensive drug treatment, as well as other factors that have not been evaluated in the present study, might have modulated this association. However, it might be argued that a causal association between arterial stiffness - disturbed baroreflex sensitivity - postural BP changes, even in subjects without pronounced vascular aging or orthostatic hypotension, is implied. © 2008 American Society of Hypertension