RECOMED: A Comprehensive Pharmaceutical Recommendation System

A comprehensive pharmaceutical recommendation system was designed based on the patients and drugs features extracted from Drugs.com and Druglib.com. First, data from these databases were combined, and a dataset of patients and drug information was built. Secondly, the patients and drugs were clustered, and then the recommendation was performed using different ratings provided by patients, and importantly by the knowledge obtained from patients and drug specifications, and considering drug interactions. To the best of our knowledge, we are the first group to consider patients conditions and history in the proposed approach for selecting a specific medicine appropriate for that particular user. Our approach applies artificial intelligence (AI) models for the implementation. Sentiment analysis using natural language processing approaches is employed in pre-processing along with neural network-based methods and recommender system algorithms for modeling the system. In our work, patients conditions and drugs features are used for making two models based on matrix factorization. Then we used drug interaction to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs by using data from 2304 patients as a training set, and then we used data from 660 patients as our validation set. After that, we used knowledge from critical information about drugs and combined the outcome of the model into a knowledge-based system with the rules obtained from constraints on taking medicine.Comment: 39 pages, 14 figures, 13 table

Ontologies in medicinal chemistry: current status and future challenges

[Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association

Scientific advances in diabetes: the impact of the innovative medicines initiative

Tese de mestrado, Regulação e Avaliação do Medicamento e Produtos de Saúde, 2020, Universidade de Lisboa, Faculdade de Farmácia.A Iniciativa sobre Medicamentos Inovadores é uma parceria público-privada entre a Comunidade Europeia, representada pela Comissão Europeia, e a Indústria Farmacêutica, representada pela Federação Europeia da Indústria Farmacêutica. Esta Iniciativa de Tecnologia Conjunta tem como objetivos acelerar o processo de investigação e desenvolvimento de medicamentos inovadores, bem como gerar novos conhecimentos científicos que promovam a integração da medicina personalizada nas doenças prioritárias definidas pela Organização Mundial de Saúde. Atualmente, no âmbito desta iniciativa foram estabelecidos dois programas, sendo que o primeiro (IMI1) decorreu entre 2008 e 2013 e teve um orçamento de 2 mil milhões de euros, enquanto que o segundo programa (IMI2) está em decurso desde 2014 e terminará em 2020 e o orçamento disponibilizado foi de 3.276 mil milhões de euros. A diabetes mellitus é uma das doenças prioritárias indicadas pela Organização Mundial de Saúde alvo de financiamento pelos programas IMI. As principais justificações para este facto prendem-se com os dados epidemiológicos da doença. No decorrer dos anos, verificou-se um aumento exponencial da taxa de prevalência desta doença a nível mundial. Esta evidência é suportada pelo facto de, entre o período de 1980 e 2014, esta taxa ter sofrido um aumento de 4.7% para 8.7%, o quadruplo do valor, em adultos com idade igual ou superior a 18 anos e também por as estimativas a 20 anos, realizadas pela Organização Mundial de Saúde, indicarem que o número total de casos existentes corresponderá a mais de 20% da população universal. Simultaneamente, constatou-se um crescimento progressivo tanto da taxa de mortalidade por diabetes bem como dos custos de saúde acarretados por esta doença. No que diz respeito à taxa de mortalidade, em 2016, a diabetes foi considerada a sétima principal causa de morte no mundo. Em termos de impacto económico, a diabetes e as complicações decorrentes desta doença, como é o caso das doenças cardiovasculares, nefropatia diabética e retinopatia diabética, impõem um grande peso económico para os sistemas de saúde. A nível mundial, os custos anuais provocados pela diabetes, entre o ano de 2007 e 2019, aumentaram de 232 mil milhões de dólares para 760 mil milhões de dólares, o que equivale a incremento de 528 mil milhões de dólares em 12 anos. Na área da Diabetes, o principal objetivo dos programas IMI1 e IMI2 é o de reduzir a tendência crescente observada na taxa de prevalência desta doença. De forma a atingir esta meta, os dois programas supramencionados primaram o financiamento de projetos cujo intuito consistia no desenvolvimento do conhecimento, medicamentos, métodos, ferramentas e modelos que facilitassem a implementação da medicina personalizada, como modelo de prática médica corrente, em doentes com diabetes. Até outubro de 2019, os programas IMI financiaram treze projetos para a área da Diabetes & Doenças Metabólicas, nomeadamente SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT e CARDIATEAM. Entre estes, o INNODIA tinha como objetivo a diabetes tipo 1, o DIRECT, EMIF e RHAPSODY tinham como foco a diabetes tipo 2, o SUMMIT, BEAT-DKD, LITMUS, Hypo-RESOLVE e CARDIATEAM estavam associados às complicações da diabetes e os restantes projetos, o StemBANCC, EBiSC, IMIDIA e IMI2PACT, estavam orientados para o desenvolvimento da vertente científica. Em geral, um investimento monetário total na ordem dos €447 249 438 foi realizado pelo IMI na área da Diabetes. Todavia, a deteção da lacuna existente na integração dos resultados produzidos pelos diferentes projetos, impulsionou a elaboração da presente dissertação intitulada de “Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative”, ou seja, Avanços Científicos na Área da Diabetes: Impacto da Iniciativa sobre Medicamentos Inovadores. Os principais objetivos estabelecidos para esta dissertação foram os de recolher os artigos publicados pelos projetos financiados e sistematizá-los nos eixos de investigação definidos na agenda estratégica do programa IMI2, mais concretamente: 1) identificação de alvos e biomarcadores, 2) novos paradigmas de ensaios clínicos, 3) medicamentos inovadores e 4) programas de adesão terapêutica centrados nos doentes. A metodologia de investigação aplicada nesta dissertação consistiu numa revisão de literatura, tendo-se utilizado como fontes de dados as páginas eletrónicas oficiais de cada projeto, o contacto com os coordenadores e co-coordenadores dos projetos e a base de dados europeia Cordis. No geral, um total de 662 citações foram identificadas, das quais 185 foram incluídas na análise realizada neste trabalho. Através da sistematização e integração dos artigos recolhidos nos projetos financiados pelo IMI, averiguou-se que para o eixo de identificação de alvos e biomarcadores, os outcomes relevantes responderam a cinco das recomendações definidas na agenda estratégica do programa IMI2, nomeadamente: 1) identificar e validar marcadores biológicos, ferramentas e ensaios, 2) identificar as determinantes que justificam a variabilidade interindividual, 3) compreender os mecanismos moleculares subjacentes à doença, 4) desenvolver uma plataforma de ensaios pré-clínicos e 5) estabelecer modelos de sistemas. De um modo geral, um vasto número de biomarcadores, ferramentas, fatores responsáveis pela heterogeneidade da população, incluindo marcadores genéticos, e mecanismos relevantes foram identificados para a diabetes tipo 1 pelo INNODIA, para a diabetes tipo 2 pelo SUMMIT, IMIDIA, DIRECT e EMIF, para as células beta pancreáticas pelo IMIDIA e RHAPSODY, para a nefropatia diabética pelo SUMMIT e BEAT-DKD, e para as doenças cardiovasculares e retinopatia diabética pelo SUMMIT. Suplementarmente, um conjunto de ferramentas e ensaios foram desenvolvidos pelos projetos StemBANCC, EBiSC e IMIDIA com o intuito de impulsionar avanços na área investigacional. Ainda neste eixo foram propostos dois modelos de estratificação dos doentes, um relativo ao controlo glicémico em doentes com diabetes tipo 1 estabelecido pelo INNODIA e outro correspondente à identificação dos subtipos de doentes com diabetes desenvolvido pela parceria BEAT-DKD/RHAPSODY. Relativamente ao eixo de ensaios clínicos, os dados analisados compreendiam propostas de novos parâmetros clínicos e de desenhos de ensaios, sendo que estes resultados visavam espelharem com maior precisão as características da subpopulação com diabetes em teste. Os dados incluídos neste eixo foram obtidos a partir dos projetos SUMMIT, DIRECT e BEAT-DKD. No que concerne ao eixo de medicamentos inovadores, as informações recolhidas dos artigos publicados pelos projetos SUMMIT, IMIDIA, DIRECT, StemBANNC, EMIF, INNODIA e BEAT-DKD consistiam na identificação de novos potenciais alvos terapêuticos bem como no desenvolvimento de novos agentes terapêuticos, ambos com a finalidade de tratar ou prevenir tanto a diabetes como as complicações associadas a esta doença. Foi ainda proposta uma nova abordagem de produção de células estaminais pluripotentes humanas em larga escala pelo StemBANCC. No que tange aos programas de maximização de resultados de saúde benéficos centrados no doente com diabetes, dois novos modelos preditivos foram desenvolvidos e validados pelo projeto DIRECT, permitindo a sua utilização como ferramentas de diagnóstico por médicos especialistas. Adicionalmente, esta dissertação tem como objetivo apresentar uma proposta de visão de complementaridade entre os treze projetos financiados pelo IMI, realçando as possíveis estratégias a adotar para a integração da medicina personalizada na prática clínica. Esta abordagem engloba a criação de indicadores biológicos e genéticos que facilitem a identificação dos indivíduos com risco elevado de desenvolver diabetes, a inclusão de ferramentas que possibilitem o diagnóstico precoce dos doentes e, por último, a seleção do tratamento apropriado às características do indivíduo, ou seja o que evidencie ser mais eficaz e seguro, suportado em modelos de estratificação de doentes, tentando desta forma retardar a progressão da doença, assim como prevenir o desenvolvimento das complicações relacionadas com a progressão da doença.Innovative Medicines Initiative (IMI) is a public-private partnership between the European Community, represented by the European Commission, and the European Federation of Pharmaceutical Industries and Associations. This joint undertaking aims at accelerating the medicines development process and generating new scientific knowledge to promote the implementation of personalized medicine for priority diseases established by the World Health Organization. Currently, two IMI programmes have been undertaken, the first one (IMI1) was carried out from 2008 until 2013 and had a budget of €2 billion, and the second one (IMI2) was developed from 2014 up to 2020 and the budget committed was up to €3.276 billion. Diabetes Mellitus is one of the World Health Organization’s priority diseases under research by the IMI programmes, mainly due to the exponential increase of its global prevalence over the years. Between 1980 and 2014, this rate quadrupled from 4.7% to 8.7% in adults aged 18 years and older and the 20 years- World Health Organization’s projections indicate that it could reach more than 20% of the population. Simultaneously, the mortality rate and the healthcare costs associated with diabetes have been increasing. Regarding mortality, diabetes was the seventh leading cause of death in 2016. In terms of economic impact, currently, diabetes and its related complications, such as cardiovascular diseases, diabetic kidney disease and diabetic retinopathy, represent a significant economic burden on the healthcare systems. Worldwide, the estimated annual costs of diabetes have increased from $232 billion to$760 billion, between 2007 and 2019. In the Diabetes field, the main aim of IMI1 and IMI2 programmes is to shorten the prevalence of this disease, through the development of knowledge and methods that enable the implementation of personalized treatment for diabetic patients. Up to October of 2019, thirteen projects were funded by IMI for Diabetes & Metabolic disorders, more precisely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. Of these, INNODIA aimed at type 1 diabetes, DIRECT, EMIF and RHAPSODY were focused on type 2 diabetes, SUMMIT, BEAT-DKD, LITMUS, Hypo- RESOLVE and CARDIATEAM were related to complications of diabetes, and the remaining projects, namely StemBANCC, EBiSC, IMIDIA and IMI2PACT, were directed to scientific research. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. However, there is a substantial lack of integration of achievements between the different projects, which prompted the development of this dissertation: “Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative”. This dissertation’ objectives were to collect the data of the funded-projects and integrate them into the following research axes: 1) target and biomarker identification, 2) innovative clinical trials paradigms, 3) innovative medicines, and 4) patient-tailored adherence programmes. The research methodology applied was a literature review and the data sources used were the official project’s websites, contacts with the project’s coordinators and co-coordinator and the CORDIS database. From the 662 citations identified, 185 were included. Through the integration of the data collected from IMI-funded projects, it was verified that for Target and Biomarker identification, the main achievements were in order to 1) identify and validate biological markers, tools and assays, 2) identify determinants of inter-individual variability, 3) understand the molecular mechanisms underlying the disease, 4) develop a platform of pre-clinical assays, and 5) develop systems’ models. Therefore, several biomarkers, tools, inter-individual variability factors, including genetic markers, and relevant pathways were proposed for type 1 diabetes by INNODIA, for type 2 diabetes by SUMMIT, IMIDIA, DIRECT and EMIF, for pancreatic β-cells by IMIDIA and RHAPSODY, for diabetic kidney disease by SUMMIT and BEAT-DKD, and for cardiovascular diseases and diabetic retinopathy by SUMMIT. Moreover, new tools and assays to improve research field were developed by StemBANCC, EBiSC and IMIDIA. Also, two models for patients’ stratification were proposed, one related to glycaemic control in patients with type 1 diabetes established by INNODIA, and another corresponding to the identification of subtypes of diabetes patients developed by BEAT-DKD/RHAPSODY. Regarding the clinical trials, the data collected SUMMIT, DIRECT and BEAT-DKD corresponds to new clinical endpoints and trial designs to accurately reflect the characteristics of the diabetic subpopulation under test. In terms of innovative medicines, information retrieved by SUMMIT, IMIDIA, DIRECT, StemBANNC, EMIF, INNODIA and BEAT-DKD consists on the identification of new therapeutic targets and the development of agents with the purpose of treatment and prevent diabetes and its related complications. Furthermore, a new approach for the large-scale production of human pluripotent stem cells was proposed by StemBANCC. Concerning the maximization of beneficial health patient-centred outcomes, two novel predictive models were developed and validated by DIRECT for diabetes to be used as screening tools by doctors. In addition, this dissertation intends to present a joint vision of the IMI-projects with strategies for integrating personalized medicine into healthcare practice. This approach involves the creation of biological and genetic indicators that can be used to identify individuals at high risk of developing diabetes, the adoption of tools that allow early diagnosis and, lastly, the selection of appropriate treatment, i.e. the safest and most effective, supported by patient stratification models, in order to prevent/delay the development of diabetic complications

The use of over-the-counter medicine and health information seeking behaviour in England

Background. In England and the UK there has been a move to provide the consumer with more choice in over the counter medicine. In recognition of the number of drugs now available without prescription, new models and frameworks are being utilised with the aim to educate the public about self-treatment. How health information is sought has also undergone transformation with the advent of the internet, the adoption and utilisation of this resource has had a significant impact on how the healthcare consumer seeks information. Aims and Methods. The aim of this study was to investigate the provision of and access to consumer health information in England, specifically with reference to over the counter medicines to promote understanding of the consumers attitudes and opinions to this type of medicine and their health information seeking behaviours. The findings of the study were used to provide recommendations to the stakeholders involved; healthcare organisations, healthcare professionals and the healthcare consumer. The research consisted of a survey (n=324) and was analysed using quantitative and qualitative methods. Results. The majority of respondents utilised over the counter medicine responsibly and with few adverse events. The General Practitioner is the main source of information and online sources the next most utilised resource. Effectiveness and following advice/recommendations were amongst the themes identified that made a treatment episode with over the counter medicines successful. Unsuccessful treatment episodes included those with escalation of symptoms. Factors governing successful health information seeking were problem solving through self diagnosis and expanding knowledge on an existing health issue. Conclusions. Over the counter medicines are a widely used commodity but respondents continue to have a heavy reliance on the general practitioner for prescription medicines, especially for minor ailments. Evidence exists that individuals utilise information seeking behaviour for self treatment and the use of over the counter medicines. However, adoption of self care models need to be increased through educating health care consumers to maximise the potential benefits of these frameworks for the stakeholders

An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories