Danielle NewbyUniversity of Oxford | OX · Department of Psychiatry
Danielle Newby
PhD Pharmacy - Computational Chemistry
Dementia prevention, Epidemiology, Real World Data, Causal Inference, Drug Repurposing
About
58
Publications
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580
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Citations since 2017
Introduction
I am an early career dementia researcher with an interdisciplinary background in epidemiology, machine learning and pharmacology. Since April 2016, I have been working as a post-doctorate researcher in the informatics team of the Translational Neuroscience and Dementia Research Group jointly led by Sir Professor Simon Lovestone and Professor Noel Buckley.
My main areas of research within the team involve the analysis of real-world data such as medical electronic health records (EHR) datasets in order to characterise the role of certain diseases, their risk factors and drug treatments with dementia risk and cognitive decline. By understanding these relationships this will provide an evidence base to support public health intervention for dementia prevention.
Additional affiliations
September 2015 - March 2016
October 2014 - August 2015
September 2013 - September 2014
Education
September 2004 - June 2007
Publications
Publications (58)
Background
Insulin resistance (IR) has previously been associated with an increased risk of developing Alzheimer’s disease (AD), although the relationship between IR and AD is not yet clear. Here, we examined the influence of IR on AD using plasma and cerebrospinal fluid (CSF) biomarkers related to IR and AD in cognitively healthy men. We also aime...
The biopharmaceutical classification system (BCS) is now well established and utilised for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multi-label classification. Unlike single label classification, multi-label classification methods predict more than one class labels at the sa...
There are currently thousands of molecular descriptors that can be calculated to represent a chemical compound. Utilising all molecular descriptors in Quantitative Structure-Activity Relationships (QSAR) modelling can result in overfitting, decreased interpretability and thus reduced model performance. Feature selection methods can overcome some of...
Class imbalance occurs frequently in drug discovery datasets. In oral absorption datasets, in the literature, there are considerably more of highly-absorbed compounds compared with poorly-absorbed compounds. This produces models that are biased towards highly-absorbed compounds which lack generalization to industry settings where more early stage d...
This study presents regression and classification models to predict human intestinal absorption of 645 drug and drug like compounds using percentage human intestinal values from the published dataset by Hou et al. (2007c). The problem with this dataset and other datasets in the literature is there are more highly than poorly absorbed compounds. Any...
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key quest...
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 artific...
The pathogenesis of dementia and depression is complex involving the interplay of genetic and environmental risk factors including diet, life‐style and the gut microbiome. Dementia and depression co‐occur and metabolomics studies may shed light on the interplay of the various risk factors. We have studied the metabolome of 118,466 individuals inclu...
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 intellige...
Clinical determinants for cardiovascular and thromboembolic (CVE) complications of COVID-19 are well-understood, but the roles of genetics and lifestyle remain unknown. We performed a prospective cohort study using UK Biobank, including 25,335 participants with confirmed SARS-CoV-2 infection between March 1, 2020, and September 3, 2021. Outcomes we...
Clinical determinants for cardiovascular and thromboembolic (CVE) complications of COVID-19 are well-understood, but the roles of genetics and lifestyle remain unknown. We performed a prospective cohort study using UK Biobank, including 25,335 participants with confirmed SARS-CoV-2 infection between March 1, 2020, and September 3, 2021. Outcomes we...
Dementia is caused by an acquired, sustained decline in brain function, leading to difficulty with everyday activities. With multiple aetiologies, clinical presentation varies, typically including problems with memory, cognition, and communication. Dementia research aims to identify risk factors, disease mechanisms and treatments. However, progress...
Introduction
Type 2 diabetes is a risk factor for dementia and Parkinson’s disease (PD). Drug treatments for diabetes, such as metformin, could be used as novel treatments for these neurological conditions. Using electronic health records from the USA (OPTUM EHR) we aimed to assess the association of metformin with all-cause dementia, dementia subt...
Introduction: Type 2 diabetes is a risk factor for dementia and Parkinson's disease (PD). Drug treatments for diabetes, such as metformin, could be used as potential novel treatments for these neurological conditions. Using medical records from the USA (OPTUM) we aimed to assess the association of metformin with all cause dementia, dementia subtype...
Augmenting traditional genome wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in available cohorts. We introduce "Genome wide association neural networks (GWANN)", a novel approach that uses neural networks (NNs) to account for nonlinear and SNP-SNP interaction effects. We applied G...
Background
Hypertension is a well‐established risk factor for cognitive impairment, brain atrophy, and dementia. However, the relationship of other types of hypertensions, such as isolated hypertension on brain health and its comparison to systolic‐diastolic hypertension (where systolic and diastolic measures are high), is still relatively unknown....
Experimental models shows that bioenergetic homeostasis changes with increasing age based on apolipoprotein E (APOE) gene. However, such link with dementia remains unclear in population. We used H1-NMR metabolome in blood from 118,021 random-selected participants in UK Biobank (n=118,021 individuals), and identified 56 metabolites associated with t...
UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to...
Globally, up to 40% of dementia cases may be prevented if several key risk factors, such as low education and obesity, are targeted. This has motivated interest into the development of risk scores that aim to quantify an individual’s risk of developing dementia within a given time frame. However, translation to a clinical setting has been hampered...
Hypertension is a well‐established risk factor for cognitive impairment, brain atrophy and dementia. Isolated hypertension is where either systolic or diastolic blood pressure is high and the other measure is normal. It is unknown the impact of isolated hypertension on brain atrophy and how it compares to non isolated hypertension (NIH). Therefore...
There is increasing interest in early metabolic changes in dementia, which may reflect the effects of genetic determinants of dementia, medical history and co‐morbidity, environmental risk factors, and aging. We studied the relation of blood‐based metabolites to the risk of dementia. The analyses are conducted in 121,389 participants of the UK Biob...
Background:
Mid-life hypertension is an established risk factor for cognitive impairment and dementia and related to greater brain atrophy and poorer cognitive performance. Previous studies often have small sample sizes from older populations that lack utilizing multiple measures to define hypertension such as blood pressure, self-report informati...
Hypertension is a well-established risk factor for cognitive impairment, brain atrophy, and dementia. However, the relationship of other types of hypertension, such as, isolated hypertension on brain health and its comparison to systolic-diastolic hypertension (where systolic and diastolic measures are high), is still relatively unknown. Due to its...
Background: Globally, up to 40% of dementia cases may be prevented if several key risk factors, such as low education and obesity, are targeted. This has motivated interest into the development of risk scores that aim to quantify an individual’s risk of developing dementia within a given
time frame. However, translation to a clinical setting has be...
Background:
Understanding adolescents' mental health during lockdown and identifying those most at risk is an urgent public health challenge. This study surveyed school pupils across Southern England during the first COVID-19 school lockdown to investigate situational factors associated with mental health difficulties and how they relate to pupils...
The multiple layers of exclusion that can be experienced by a child at school and the relationship of this to mental well-being is the focus of this paper. The relationship between specific mental health problems and school exclusion is discussed. Data gathered from 1648 English school-aged students in 2019 who participated in the OxWell school men...
INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regress...
Objectives
Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.
Methods
We performed a...
Cardiovascular disease affects multiple organs beyond those of the cardiovascular system including the brain. However, the mechanisms linking cardiovascular disease and cognitive decline are still poorly understood. Studies suggest specific cardiovascular medications targeting different mechanisms may slow cognitive decline and reduce dementia risk...
BACKGROUND
Findings from randomized controlled trials have yielded conflicting results on the association between blood pressure (BP) and Dementia traits. We tested the hypothesis that a causal relationship exists between systolic (SBP) and/or diastolic (DBP) blood pressure and risk of Alzheimer’s Disease (AD).
METHODS
We performed a Generalized S...
Background
Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the...
Objectives Concern has been raised in the rheumatological community regarding recent regulatory warnings that hydroxychloroquine used in the COVID-19 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation, or psychosis associated with hydroxychloroquine as used for rheumatoid...
Background: Hydroxychloroquine has recently received Emergency Use Authorization by the FDA and is currently prescribed in combination with azithromycin for COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin.
Methods: New user cohort studies were conducted including 16 severe adverse events (...
Background: Hydroxychloroquine has recently received Emergency Use Authorization by the FDA and is currently prescribed in combination with azithromycin for COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin. Methods: New user cohort studies were conducted including 16 severe adverse events (...
Background:
Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk.
Methods:
A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 6...
Inflammatory processes have been shown to play a role in dementia. To understand this role we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk.METHODS A retrospective matched case-control study, of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) were...
Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer’s disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) Aβ and tau tests.
Objective: To discover a plasma proteomic signature associated with CSF and PET measures of...
Observational studies have shown consistently increased likelihood of dementia or mild cognitive impairment diagnoses in people with higher air pollution exposure history, but evidence has been less consistent for associations with cognitive test performance. We estimated the association between baseline neighbourhood-level exposure to airborne pol...
Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organism...
The biopharmaceutics classification system (BCS) is now well established and utilised for the development and biowaviers of immediate oral dosage forms [1]. The BCS classifies drugs into one of four classes according to their measured permeability and solubility. There is now a growing interest in drug discovery for a provisional in silico predicti...
There are currently thousands of molecular descriptors that can be calculated to represent a chemical compound. Utilising all molecular descriptors in Quantitative Structure-Activity Relationships (QSAR) modelling can result in overfitting decreased interpretability and reduction in model performance. Feature selection methods offer a way to overco...
Permeability measured using cell based assays can give an indication of human intestinal absorption (HIA). The correlation between permeability and oral absorption can be studied in order to define a permeability threshold which can be used to predict high or low oral absorption [1]. This research uses decision trees (CART analysis) to determine a...
Oral administration is the most popular and convenient route for drug delivery. With the cost of bringing new medicines to the market reaching a billion dollars pharmaceutical companies look for ways to reduce time and costs. In vitro cell based assays that measure permeability are often used in drug discovery as relatively high-throughput indicato...
In silico modeling of oral absorption is a cost effective way of removing poorly-absorbed compounds before synthesis, in drug discovery. However there are many physiochemical and physiological factors that can affect absorption. On top of this oral absorption datasets contain more highly-absorbed than poorly-absorbed compounds, which creates an unb...
In silico modeling of oral absorption is a complicated process due to the many factors that can affect and influence it. Knowledge of oral intestinal absorption is vital as it is the prerequisite to oral bioavailability, where a high value is vital for oral drug administration. Therefore in silico modeling of intestinal absorption allows drug disco...
Questions
Questions (6)
Just wondering how i can calculate molecular descriptors for heavy metal complexes for example vitamin b12 contains a cobalt in its structure? Using MOE software as far as i am aware as soon as i wash the molecules it will remove the heavy metal leaving the structure without the metal which i would like to keep! I know people in the literature often remove compounds like these in their datasets but was wondering if it is actually possible to include them and calculate descriptors for the complex as a whole. Thanks for any thoughts, comments, suggestions! Danielle
Hi,
I am looking for some advice on a potential pipeline for reading in compounds and calculating molecular descriptors using KNIME. The nodes have to be freely available. If anyone has any recommendations on what nodes would be best and also nodes that calculate molecular descriptors i would appreciate the advice.
I am looking to use KNIME to read in my compounds in a SMILES format. I would need to de salt and optimise the geometry of the compounds (do i need to remove or add in explicit hydrogens?) to then calculate 2D and 3D descriptors. I am looking for a pipeline that allows easy checking and picks up any errors on reading the compounds in as well. I have been looking at RDkit, CDK and padel for descriptors for calculation.
Any advice would be greatly appreciated.
Kind Regards
Danielle Newby
Dear all,
I would like to know if MOE has the capabilities to carry out flexible docking. What I mean by this is when you allow the binding site (only the residues at the binding site) to be flexible during the docking. I know other software can do this however I was wondering if MOE could also do it? If anyone has experience with this i would greatly appreciate it.
Thanks.
Dear All,
I wish to calculate structural fingerprint keys for a set of drug and drug like compounds. I have two questions:
What would be the best structural fingerprints to use and why? so is MACCS better than the Pubchem fingerprint keys for example
Are there any publications which compare structural fingerprints for one data set and highlight possibly (with an objective measure) which fingerprints would be better to use.
Any literature relating to this topic will be greatly appreciated.
Thanks for any help
Danielle
Where can i find the best crystal structure for MRP1 (ABCC1)? Ideally with the binding site defined in a format that i can upload into MOE software. The Structure can be from bacterial/rodent/mammal or even a homology model.
I have tried looking at PDB and uniprot but with not much luck with obtaining the actual sequence for the protein. In particular for uniprot there are 15 sequences in mammals however i do not know which one to choose or how to extract the sequence to read it into the MOE software.
Any publications with this information but with access to the protein sequence will be greatly appreciated too!
Thanks for any help
Danielle
I would like a simple explanation of the GCUT and BCUT molecular descriptors used in the software MOE. The explanations on the MOE website are too complicated. This is the explanation of BCUT:
The BCUT descriptors [Pearlman 1998] are calculated from the eigenvalues of a modified adjacency matrix. Each ij entry of the adjacency matrix takes the value 1/sqrt(bij) where bij is the formal bond order between bonded atoms i and j
A simpler explanation would be really helpful.
