Data mining methods for the prediction of intestinal absorption using QSAR

Oral administration is the most common route for administration of drugs. With the growing cost of drug discovery, the development of Quantitative Structure-Activity Relationships (QSAR) as computational methods to predict oral absorption is highly desirable for cost effective reasons. The aim of this research was to develop QSAR models that are highly accurate and interpretable for the prediction of oral absorption. In this investigation the problems addressed were datasets with unbalanced class distributions, feature selection and the effects of solubility and permeability towards oral absorption prediction. Firstly, oral absorption models were obtained by overcoming the problem of unbalanced class distributions in datasets using two techniques, under-sampling of compounds belonging to the majority class and the use of different misclassification costs for different types of misclassifications. Using these methods, models with higher accuracy were produced using regression and linear/non-linear classification techniques. Secondly, the use of several pre-processing feature selection methods in tandem with decision tree classification analysis – including misclassification costs – were found to produce models with better interpretability and higher predictive accuracy. These methods were successful to select the most important molecular descriptors and to overcome the problem of unbalanced classes. Thirdly, the roles of solubility and permeability in oral absorption were also investigated. This involved expansion of oral absorption datasets and collection of in vitro and aqueous solubility data. This work found that the inclusion of predicted and experimental solubility in permeability models can improve model accuracy. However, the impact of solubility on oral absorption prediction was not as influential as expected. Finally, predictive models of permeability and solubility were built to predict a provisional Biopharmaceutic Classification System (BCS) class using two multi-label classification techniques, binary relevance and classifier chain. The classifier chain method was shown to have higher predictive accuracy by using predicted solubility as a molecular descriptor for permeability models, and hence better final provisional BCS prediction. Overall, this research has resulted in predictive and interpretable models that could be useful in a drug discovery context

Work hard, party harder:drug use and sexual behaviour in young British casual workers in Ibiza, Spain

Background: Every summer, young people flock to nightlife-focused holiday resorts around the world to find casual work. Despite being exposed to hedonistic environments, often for several months, little is known about their substance use, sexual activity and health service needs over this extended amount of time abroad. Methods: A short anonymous questionnaire examining alcohol and drug use, sexual behaviour and use of health services was administered to young British casual workers aged 16–35 in San Antonio, Ibiza (n = 171). Results: 97.7% of casual workers used alcohol in Ibiza, and the majority (85.3%) used drugs. Almost half (43.5%) of all participants used a drug in Ibiza that they had never used in the UK. Most casual workers arrived in Ibiza without a partner or spouse (86.5%). Of these, 86.9% had sex during their stay and 50.0% had unprotected sex; often while under the influence of alcohol. Only 14.3% of those having unprotected sex with a new partner sought a sexual health check-up in Ibiza, although 84.1% intended to do this on their return to the UK. Conclusion: Substance use and sexual risk taking is widespread among young British casual workers in Ibiza. Such international nightlife resorts represent key settings for substance-related health and social problems, and for the international spread of sexually transmitted infections. Addressing the health needs of casual workers and the environments that permit and promote their excessive behaviour requires collaboration between authorities in home and destination countries and the tourism industry

Trends of use and characterisation of anti-dementia drugs users:a large multinational-network population-based study

Background: An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. Setting: Primary care data (country/database) from the UK/CPRD-GOLD (2007–20), Spain/SIDIAP (2010–20) and the Netherlands/IPCI (2008–20), standardised to a common data model. Methods: Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). Results: We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI. In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891–32,862)) to 2010 (17,793 (17,083–18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021–25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199–52,855)) to 2020 (14,571 (14,109–15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967–29,031)) to 2020 (4763 (4176–5409)). Subjects aged ≥65–79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. Conclusions: Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.</p

Trends of use and characterisation of anti-dementia drugs users:a large multinational-network population-based study

Background: An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. Setting: Primary care data (country/database) from the UK/CPRD-GOLD (2007–20), Spain/SIDIAP (2010–20) and the Netherlands/IPCI (2008–20), standardised to a common data model. Methods: Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). Results: We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI. In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891–32,862)) to 2010 (17,793 (17,083–18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021–25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199–52,855)) to 2020 (14,571 (14,109–15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967–29,031)) to 2020 (4763 (4176–5409)). Subjects aged ≥65–79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. Conclusions: Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.</p

Tracing tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology

Background: Use of personal sensing to predict mental health risk has sparked interest in adolescent psychiatry, offering a potential tool for targeted early intervention. Objectives: We investigated the preferences and values of UK adolescents with regard to use of digital sensing information, including social media and internet searching behaviour. We also investigated the impact of risk information on adolescents’ self-understanding. Methods: Following a Design Bioethics approach, we created and disseminated a purpose-built digital game (www.tracingtomorrow.org) that immersed the player-character in a fictional scenario in which they received a risk assessment for depression Data were collected through game choices across relevant scenarios, with decision-making supported through clickable information points. Findings: The game was played by 7337 UK adolescents aged 16–18 years. Most participants were willing to personally communicate mental health risk information to their parents or best friend. The acceptability of school involvement in risk predictions based on digital traces was mixed, due mainly to privacy concerns. Most participants indicated that risk information could negatively impact their academic self-understanding. Participants overwhelmingly preferred individual face-to-face over digital options for support. Conclusions: The potential of digital phenotyping in supporting early intervention in mental health can only be fulfilled if data are collected, communicated and actioned in ways that are trustworthy, relevant and acceptable to young people. Clinical implications: To minimise the risk of ethical harms in real-world applications of preventive psychiatric technologies, it is essential to investigate young people’s values and preferences as part of design and implementation processes

Validation of UK Biobank data for mental health outcomes : a pilot study using secondary care electronic health records

The study was funded by the MRC Pathfinder Grant (MC_PC_17215); the National Institute for Health Research’s (NIHR) Oxford Health Biomedical Research Centre (BRC-1215-20005) and the Virtual Brain Cloud from European Commission (grant no. H2020SC1-DTH-2018-1). This work was supported by the UK Clinical Record Interactive Search (UK-CRIS) system funded by the National Institute for Health Research (NIHR) and the Medical Research Council, with the University of Oxford, using data and systems of the NIHR Oxford Health Biomedical Research Centre (BRC-1215-20005).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 UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.Peer reviewe

Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection

Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.</p

Collateral effects of the COVID-19 pandemic on endocrine treatments for breast and prostate cancer in the UK: a cohort study

Background:: The COVID-19 pandemic affected cancer screening, diagnosis and treatments. Many surgeries were substituted with bridging therapies during the initial lockdown, yet consideration of treatment side effects and their management was not a priority. Objectives:: To examine how the changing social restrictions imposed by the pandemic affected incidence and trends of endocrine treatment prescriptions in newly diagnosed (incident) breast and prostate cancer patients and, secondarily, endocrine treatment-related outcomes (including bisphosphonate prescriptions, osteopenia and osteoporosis), in UK clinical practice from March 2020 to June 2022. Design:: Population-based cohort study using UK primary care Clinical Practice Research Datalink GOLD database. Methods:: There were 13,701 newly diagnosed breast cancer patients and 12,221 prostate cancer patients with ⩾1-year data availability since diagnosis between January 2017 and June 2022. Incidence rates (IR) and incidence rate ratios (IRR) were calculated across multiple time periods before and after lockdown to examine the impact of changing social restrictions on endocrine treatments and treatment-related outcomes, including osteopenia, osteoporosis and bisphosphonate prescriptions. Results:: In breast cancer patients, aromatase inhibitor (AI) prescriptions increased during lockdown versus pre-pandemic [IRR: 1.22 (95% confidence interval (CI): 1.11–1.34)], followed by a decrease post-first lockdown [IRR: 0.79 (95% CI: 0.69–0.89)]. In prostate cancer patients, first-generation antiandrogen prescriptions increased versus pre-pandemic [IRR: 1.23 (95% CI: 1.08–1.4)]. For breast cancer patients on AIs, diagnoses of osteopenia, osteoporosis and bisphosphonate prescriptions were reduced across all lockdown periods versus pre-pandemic (IRR range: 0.31–0.62). Conclusion:: During the first 2 years of the pandemic, newly diagnosed breast and prostate cancer patients were prescribed more endocrine treatments compared to pre-pandemic due to restrictions on hospital procedures replacing surgeries with bridging therapies. But breast cancer patients had fewer diagnoses of osteopenia and osteoporosis and bisphosphonate prescriptions. These patients should be followed up in the coming years for signs of bone thinning. Evidence of poorer management of treatment-related side effects will help assess resource allocation for patients at high risk for bone-related complications

High blood pressure and risk of dementia : a two-sample Mendelian randomization study in the UK biobank

This work was supported by Janssen Research and Development , LLC (of Johnson & Johnson).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 BP (SBP) and/or diastolic BP (DBP) and risk of Alzheimer's disease (AD). Methods: We performed a generalized summary Mendelian randomization (GSMR) analysis using summary statistics of a genome-wide association study meta-analysis of 299,024 individuals of SBP or DBP as exposure variables against three different outcomes: 1) AD diagnosis (International Genomics of Alzheimer's Project), 2) maternal family history of AD (UK Biobank), and 3) paternal family history of AD (UK Biobank). Finally, a combined meta-analysis of 368,440 individuals that included these three summary statistics was used as final outcome. Results: GSMR applied to the International Genomics of Alzheimer's Project dataset revealed a significant effect of high SBP lowering the risk of AD (βGSMR = −0.19, p =.04). GSMR applied to the maternal family history of AD UK Biobank dataset (SBP [βGSMR = −0.12, p =.02], DBP [βGSMR = −0.10, p =.05]) and to the paternal family history of AD UK Biobank dataset (SBP [βGSMR = −0.16, p =.02], DBP [βGSMR = −0.24, p = 7.4 × 10−4]) showed the same effect. A subsequent combined meta-analysis confirmed the overall significant effect for the other SBP analyses (βGSMR = −0.14, p =.03). The DBP analysis in the combined meta-analysis also confirmed a DBP effect on AD (βGSMR = −0.14, p =.03). Conclusions: A causal effect exists between high BP and a reduced late-life risk of AD. The results were obtained through careful consideration of confounding factors and the application of complementary MR methods on independent cohorts.Peer reviewe

The impact of the UK COVID-19 lockdown on the screening, diagnostics and incidence of breast, colorectal, lung and prostate cancer in the UK: a population-based cohort study

Introduction: The COVID-19 pandemic had collateral effects on many health systems. Cancer screening and diagnostic tests were postponed, resulting in delays in diagnosis and treatment. This study assessed the impact of the pandemic on screening, diagnostics and incidence of breast, colorectal, lung, and prostate cancer; and whether rates returned to pre-pandemic levels by December, 2021. Methods: This is a cohort study of electronic health records from the United Kingdom (UK) primary care Clinical Practice Research Datalink (CPRD) GOLD database. The study included individuals registered with CPRD GOLD between January, 2017 and December, 2021, with at least 365 days of clinical history. The study focused on screening, diagnostic tests, referrals and diagnoses of first-ever breast, colorectal, lung, and prostate cancer. Incidence rates (IR) were stratified by age, sex, and region, and incidence rate ratios (IRR) were calculated to compare rates during and after lockdown with rates before lockdown. Forecasted rates were estimated using negative binomial regression models. Results: Among 5,191,650 eligible participants, the first lockdown resulted in reduced screening and diagnostic tests for all cancers, which remained dramatically reduced across the whole observation period for almost all tests investigated. There were significant IRR reductions in breast (0.69 [95% CI: 0.63-0.74]), colorectal (0.74 [95% CI: 0.67-0.81]), and prostate (0.71 [95% CI: 0.66-0.78]) cancer diagnoses. IRR reductions for lung cancer were non-significant (0.92 [95% CI: 0.84-1.01]). Extrapolating to the entire UK population, an estimated 18,000 breast, 13,000 colorectal, 10,000 lung, and 21,000 prostate cancer diagnoses were missed from March, 2020 to December, 2021. Discussion: The UK COVID-19 lockdown had a substantial impact on cancer screening, diagnostic tests, referrals, and diagnoses. Incidence rates remained significantly lower than pre-pandemic levels for breast and prostate cancers and associated tests by December, 2021. Delays in diagnosis are likely to have adverse consequences on cancer stage, treatment initiation, mortality rates, and years of life lost. Urgent strategies are needed to identify undiagnosed cases and address the long-term implications of delayed diagnoses