Biological mechanisms of aging predict age-related disease multimorbidities in patients

Genetic, environmental and pharmacological interventions into the aging process can confer resistance to a multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that mechanisms of aging might contribute to patterns of multimorbidity in humans, and hence could be targeted to prevent multiple conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation, and found strong, non-random associations between age-related diseases and aging mechanisms, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signalling pathway and activity of the ERK1/2 pathway played a significant role across multiple aging mechanisms and multiple, diverse age-related diseases. Mechanisms of aging therefore contribute to multiple age-related diseases and to patterns of human age-related multimorbidity, and could potentially be targeted to prevent more than one age-related condition in the same patient

Clinical trial design and dissemination: comprehensive analysis of clinicaltrials.gov and PubMed data since 2005

OBJECTIVE: To investigate the distribution, design characteristics, and dissemination of clinical trials by funding organisation and medical specialty. DESIGN: Cross sectional descriptive analysis. DATA SOURCES: Trial protocol information from clinicaltrials.gov, metadata of journal articles in which trial results were published (PubMed), and quality metrics of associated journals from SCImago Journal and Country Rank database. SELECTION CRITERIA: All 45 620 clinical trials evaluating small molecule therapeutics, biological drugs, adjuvants, and vaccines, completed after January 2006 and before July 2015, including randomised controlled trials and non-randomised studies across all clinical phases. RESULTS: Industry was more likely than non-profit funders to fund large international randomised controlled trials, although methodological differences have been decreasing with time. Among 27 835 completed efficacy trials (phase II-IV), 15 084 (54.2%) had disclosed their findings publicly. Industry was more likely than non-profit trial funders to disseminate trial results (59.3% (10 444/17 627) v 45.3% (4555/10 066)), and large drug companies had higher disclosure rates than small ones (66.7% (7681/11 508) v 45.2% (2763/6119)). Trials funded by the National Institutes of Health (NIH) were disseminated more often than those of other non-profit institutions (60.0% (1451/2417) v 40.6% (3104/7649)). Results of studies funded by large drug companies and NIH were more likely to appear on clinicaltrials.gov than were those from non-profit funders, which were published mainly as journal articles. Trials reporting the use of randomisation were more likely than non-randomised studies to be published in a journal article (6895/19 711 (34.9%) v 1408/7748 (18.2%)), and journal publication rates varied across disease areas, ranging from 42% for autoimmune diseases to 20% for oncology. CONCLUSIONS: Trial design and dissemination of results vary substantially depending on the type and size of funding institution as well as the disease area under study

Genetic drug target validation using Mendelian randomisation

Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the 'no horizontal pleiotropy assumption' is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses

Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target's expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process

Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

Copyright © 2021 The Author(s). Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target’s expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process.The authors are grateful to the studies and consortia that provided summary association results and to the participants of the biobanks and research cohorts. This research has been conducted using the UK Biobank Resource under Application Number 12113. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. M.G.M. is supported by a BHF Fellowship FS/17/70/33482. A.F.S. is supported by BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. C.F. and A.F.S. received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. A.D.H. is an NIHR Senior Investigator. We further acknowledge support from the Rosetrees Trust. The UCLEB Consortium is supported by a British Heart Foundation Program Grant (RG/10/12/28456). M.K. was supported by grants from the Wellcome Trust, UK (221854/Z/20/Z), the UK Medical Research Council (R024227 and S011676), the National Institute on Aging, NIH (R01AG056477 and RF1AG062553), and the Academy of Finland (311492). AH receives support from the British Heart Foundation, the Economic and Social Research Council (ESRC), the Horizon 2020 Framework Program of the European Union, the National Institute on Aging, the National Institute for Health Research University College London Hospitals Biomedical Research Centre, the UK Medical Research Council and works in a unit that receives support from the UK Medical Research Council. A.G. is funded by the Member States of EMBL. P.C. is supported by the Thailand Research Fund (MRG6280088). D.A.L. is supported by a British Heart Foundation Chair (CH/F/20/90003) and British Heart Foundation grant (AA/18/7/34219), is a National Institute of Health Research Senior Investigator (NF-0616-10102) and works in a Unit that receives support from the University of Bristol and UK Medical Research Council (MC_UU_00011/6). This work was funded in part by the UKRI and NIHR through the Multimorbidity Mechanism and Therapeutics Research Collaborative (MR/V033867/1)

Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease

The preliminary meta-analysis of RCT data were presented at BPS 2018 by NH. The preprint version of this paper has been deposited on medrxiv: https://doi.org/10.1101/2020.09.07.20189571.Copyright © 2021 The Author(s). Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer’s disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration.This research has been conducted using the UK Biobank Resource under Application Number 12113. The authors are grateful to UK Biobank participants. We gratefully acknowledge the support of UCLEB and CHARGE. Funding and role of funding sources: A.F.S. is supported by BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. C.F. and A.F.S. received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.G.M. is supported by a BHF Fellowship FS/17/70/33482. A.D.H. is an NIHR Senior Investigator. This work was supported by the UKRI/NIHR Strategic Priorities Award in Multimorbidity Research (MR/V033867/1). This work was additionally supported by a grant [R01 LM010098] from the National Institutes of Health (USA). We further acknowledge support from the Rosetrees and Stoneygate Trust. The UCLEB Consortium is supported by a British Heart Foundation Program Grant (RG/10/12/28456). T.R.G. receives support from the UK Medical Research Council (MC_UU_00011/4). D.O.M.K. is supported by the Dutch Science Organization (ZonMW-VENI Grant 916.14.023). A D.H. receives support from the UK Medical Research (MC_UU_12019/1). M.K. is supported by the Wellcome Trust (221854/Z/20/Z), the UK Medical Research Council (MR/S011676/1, MR/R024227/1), National Institute on Aging (NIH), US (R01AG062553), and the Academy of Finland (311492). D.A.L. is supported by a Bristol BHF Accelerator Award (AA/18/7/34219) and BHF Chair (CH/F/20/90003) and works in a unit that receives support from the University of Bristol and the UK Medical Research Council (MC_UU_00011/6). D.A.L. is a National Institute of Health Research Senior Investigator (NF-0616-10102). N.F. is supported by the National Institutes of Health (R01-MD012765, R01-DK117445, R21- HL140385). P.C. is supported by the Thailand Research Fund (MRG6280088. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant R01HL105756

Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease

Despite being studied in clinical trials, CETP inhibitors are not yet an approved treatment for coronary heart disease. Here, by analyzing results from clinical trials and drug target mendelian randomization studies, the authors demonstrate that previous failure of CETP inhibitors are likely compound and not drug target-related.Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer's disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration.Clinical epidemiolog

RNA folding using quantum computers.

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots