Pharmaceutical Medicine最新文献

{"title":"Major Pharmaceutical Conferences and Courses: February to March 2023","authors":"","doi":"10.1007/s40290-022-00449-5","DOIUrl":"https://doi.org/10.1007/s40290-022-00449-5","url":null,"abstract":"","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44908741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Pharmaceutical Year That Was, 2022","authors":"A. Fox","doi":"10.1007/s40290-022-00448-6","DOIUrl":"https://doi.org/10.1007/s40290-022-00448-6","url":null,"abstract":"","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44746687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are the Current Processes and Regulations Fit for Purpose to Deliver Novel Therapies During Pandemics? A Perspective on COVID-19 from the UK.","authors":"Rajmeet Jandu,&nbsp;Carl Naraynassamy,&nbsp;Nadarajah Sreeharan","doi":"10.1007/s40290-022-00439-7","DOIUrl":"https://doi.org/10.1007/s40290-022-00439-7","url":null,"abstract":"<p><p>The COVID-19 pandemic was the first 'stress test' to assess whether the current regulations in the United Kingdom (UK) are fit for purpose to develop novel therapies during pandemics. It saw innovations and collaborations across the spectrum of the drug development and regulatory pathways, including extraordinary collaborations between the various stakeholders involved in the process, the repositioning of medicines, the deployment of multi-arm, multi-interventional adaptive trials, the institution of operational simplicity and flexibility across various trial activities, and regulatory innovations. The question arises whether the innovative flexibilities and the urgency that were instituted could have resulted in compromises to the integrity of the process. An assessment of the conduct of the RECOVERY trial and the speedy approval of dexamethasone by the UK Medicines and Healthcare products Regulatory Agency demonstrates that no compromises were made to the ethical and scientific integrity of the process. Lessons learnt could be applied for future pandemics and to enhance R&D productivity and contribute to global health by improving access to medicines, especially in low- and middle-income countries and for neglected or rare diseases. What is needed is not a major transformation in the process but the flexible adaptation of existing regulations to reduce bureaucracy and handover times. Arriving at an optimal balance between scientific standards, regulations and commercial conflicts of interest will pose considerable challenges but what the COVID-19 pandemic has shown is that where there is will, there is always a way.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9333354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40653595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.","authors":"Benjamin M Knisely,&nbsp;Qais Hatim,&nbsp;Monifa Vaughn-Cooke","doi":"10.1007/s40290-022-00434-y","DOIUrl":"https://doi.org/10.1007/s40290-022-00434-y","url":null,"abstract":"<p><strong>Background: </strong>The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the public when a medication is suspected to have caused an AE. The FDA monitors these reports to identify drug safety issues that were undetected during the premarket evaluation of these products. These reports contain patient narratives that provide information regarding the AE that needs to be coded using standardized terminology to enable aggregation of reports for further review. Additionally, the FDA collects structured drug product labels (SPLs) that facilitate standardized distribution of information regarding marketed medical products. Manufacturers are currently not required to code labels with associated AEs.</p><p><strong>Objectives: </strong>Approaches for automated classification of reports by preferred terminology could enhance regulatory efficiency. The goal of this work was to assess the suitability of manually annotated FDA FAERS and SPL data sets to be subjected to predictive modeling.</p><p><strong>Methods: </strong>A recurrent neural network (RNN) was proposed as a proof-of-concept model for automated extraction of preferred AE terminology. A separate RNN was fit and cross-validated on two regulatory data sets with varying properties. First, the researchers trained and cross-validated a model on 325 annotated FAERS patient narratives for a sample of AE terms. A model was then trained and validated on a data set of 100 SPLs.</p><p><strong>Results: </strong>Model cross-validation results for product labels demonstrated that the model performed at least as well as more conventional models for all but one of the terms selected based on F1-score. Model results for the FAERS data set were mixed.</p><p><strong>Conclusions: </strong>This work successfully demonstrated a proof-of-concept machine learning approach to automatically detect AEs in several textual regulatory data sets to support post-market regulatory activities. Limited instances of each AE class likely prohibited models from generalizing data effectively. Additional data may permit more robust validation.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40532063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pricing and Market Access Challenges in the Era of One-Time Administration Cell and Gene Therapies.","authors":"Marco T Sabatini,&nbsp;Tia Xia,&nbsp;Mark Chalmers","doi":"10.1007/s40290-022-00443-x","DOIUrl":"https://doi.org/10.1007/s40290-022-00443-x","url":null,"abstract":"<p><p>With a large number of one-time administration cell and gene therapies expected to come to the market in the coming years, there is a renewed need to understand the existing and future challenges that such modalities bring about, especially as it relates to their assessment of value, pricing and access. Payer, health technology assessment (HTA) bodies and manufacturers alike are faced with a number of unprecedented challenges stemming from the fact that such therapies are 'one-time' and/or have curative intent, but often lack sufficient evidence to support such claims at the time of launch (i.e., during pricing and access negotiations). There are a number of different approaches to assessing economic value for cell and gene therapies across regions (e.g., US vs Europe), which ultimately lead to further disconnect in pricing and reimbursement outcomes across countries; yet, in many cases, affordability concerns relating to high upfront costs are raised by providers. To that end, cell and gene therapies have been frequently criticized by payers for their 'high sticker price' based on relatively limited evidence to support durability claims. New contracting solutions are increasingly being employed to overcome concerns specifically relating to the durability of clinical benefit, the comparative effectiveness of a therapy and affordability (i.e., the one-time high cost of therapy). Indeed, recent launches of cell and gene therapies have often leveraged outcome-based agreements, instalments, coverage with evidence generation, subscription models, stop-loss and payer reinsurance, etc. to mitigate concerns from payers and providers and drive access. In this paper, we aim to review challenges for cell and gene therapies from a pricing and access perspective and explore the growing role of innovative contracting solutions to overcome aforementioned challenges.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40631620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Randomised Controlled Trial at the Intersection of Research Ethics and Innovation.","authors":"Torbjörn Callréus","doi":"10.1007/s40290-022-00438-8","DOIUrl":"10.1007/s40290-022-00438-8","url":null,"abstract":"<p><p>The randomised controlled trial (RCT) has been considered for a long time as the gold standard for evidence generation to support regulatory decision making for medicines. The randomisation procedure involves an ethical dilemma since it means leaving the treatment choice to chance. Although currently contested, the ethical justification for the RCT that has gained widespread acceptance is the notion of 'clinical equipoise'. This state exists when \"there is no consensus within the expert clinical community about the comparative merits of the alternatives to be tested\"; it is argued that this confers the ethical grounds for the conduct of an RCT. The prominent position of the RCT is being challenged by new therapeutic modalities for which this study design may be unsuitable. Moreover, alternative approaches to evidence generation represent another area where innovation may have implications for the relevance of the RCT. Against the backdrop of the debate around the equipoise principle and some recent therapeutic and data analytical innovations, the aim of this article is to explore the current standing of the RCT from a regulatory perspective.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40633024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Research Charities and Biopharmaceutical Companies as Partners in Patient-Centred R&D.","authors":"Tina Flatau,&nbsp;Julie Greenfield,&nbsp;Brian Dickie,&nbsp;Oli Rayner,&nbsp;Helen Matthews,&nbsp;John Wise","doi":"10.1007/s40290-022-00442-y","DOIUrl":"https://doi.org/10.1007/s40290-022-00442-y","url":null,"abstract":"<p><p>Life science research and development (R&D) companies are all too aware of the importance of patient perspectives but also of the barriers to engaging directly with patients, not least compliance, complex technical and regulatory issues, and the need to meet multifaceted expectations. Medical research charities (MRCs), highly technical and professional organisations, work directly with patients; they represent an expert resource for the science of their field, for disease-related patient advocacy issues and to advise and assist R&D companies in devising meaningful trials. The Pistoia Alliance, a non-profit organisation facilitating life sciences R&D, gathered a number of UK MRCs focused on complex lifelong conditions. The group used workshops and an opinion questionnaire for a snapshot of how the charities believe their knowledge and patient experiences could contribute insights and efficiencies to commercial R&D. MRCs argued that for chronic conditions, the patient perspective is vital in facilitating and de-risking trials, promoting patient motivation, compliance and study viability. MRCs and the patients they represent want to see successful trials, and it is in everyone's interest that well considered studies can proceed. Today, with remote assessments, consumer wearables and digital health technologies, MRCs and patients are already collating substantial data sets that are relevant to quality-of-life benefits, regulatory and value assessments, all of great interest to biopharmaceutical companies. In turn, MRCs would benefit from the experience of biopharma in generating clinical data and implementing novel technologies.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40619070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.","authors":"Maribel Salas,&nbsp;Jan Petracek,&nbsp;Priyanka Yalamanchili,&nbsp;Omar Aimer,&nbsp;Dinesh Kasthuril,&nbsp;Sameer Dhingra,&nbsp;Toluwalope Junaid,&nbsp;Tina Bostic","doi":"10.1007/s40290-022-00441-z","DOIUrl":"https://doi.org/10.1007/s40290-022-00441-z","url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development.</p><p><strong>Objective: </strong>The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review.</p><p><strong>Methods: </strong>Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded.</p><p><strong>Results: </strong>Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source.</p><p><strong>Conclusion: </strong>Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40655126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Major Pharmaceutical Conferences and Courses: December 2022 to January 2023","authors":"","doi":"10.1007/s40290-022-00445-9","DOIUrl":"https://doi.org/10.1007/s40290-022-00445-9","url":null,"abstract":"","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43332442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Performance of the Gulf Cooperation Council Centralised Regulatory Review Process: Strategies to Improve Product Authorisation Efficiency and Quality.","authors":"Hajed M Hashan,&nbsp;Sarah K Al-Muteb,&nbsp;Ibrahim A Alismail,&nbsp;Othman N Alsaleh,&nbsp;Zakiya M Alkherb,&nbsp;Neil McAuslane,&nbsp;Stuart R Walker","doi":"10.1007/s40290-022-00432-0","DOIUrl":"https://doi.org/10.1007/s40290-022-00432-0","url":null,"abstract":"<p><strong>Background: </strong>The Gulf Centralised Committee for Drug Registration (GCC-DR), as part of the Gulf Health Council (GHC), enables the consolidated registration of pharmaceutical products throughout the member states of the Gulf Cooperation Council.</p><p><strong>Objectives: </strong>The objectives of this study were to provide an update of the performance of the GCC-DR centralised procedure; evaluate the review times for new products submitted to the GCC Centralised Registration between January 2015 and December 2020; assess the impact of applying facilitated regulatory pathways and implementing a reliance strategy; identify the strengths and weaknesses of the centralised review process; and propose strategies that could enhance the GCC regulatory review process leading to improved access to medicines for patients.</p><p><strong>Methods: </strong>A standardised data collection template enabled the structured documentation of information collected by the Senior Regulatory Affairs and Regulatory Affairs Specialists from the Executive Board of the Health Ministers Council for GCC States to determine the GHC structure, resources, review models and milestones and timelines. The total number of applications approved was provided together with the average yearly timelines for new active substances and generics from January 2015 to December 2020 including both scientific assessment time from the agency as well as applicant response time to questions raised. Actual approval times for each product were calculated from the date of submission to the date of approval.</p><p><strong>Results: </strong>The fewest (58) new products were approved in 2019 and the most (200) in 2020. The average review times for new medicines were the longest (838 calendar days) in 2015 and the shortest (321 calendar days) in 2019. Important changes recently implemented include an increase in the number of GCC-DR meetings, adoption of a standardised electronic common technical document and GCC regulatory review template, removal of authorisation dependence on pricing agreements and introduction of a reliance strategy. Additional recommendations include Executive Committee mandates for dossier review, target times for dossier validation, scientific review and Expert Committee recommendation and training for quality decision making.</p><p><strong>Conclusions: </strong>GCC procedures and decision-making processes have been positively influenced by a variety of expert reviewers, unified guidelines and the implementation of a reliance strategy. Certain barriers must still be overcome to enhance the quality of the review, and to shorten regulatory review times without compromising the scientific robustness of the review.</p>","PeriodicalId":19778,"journal":{"name":"Pharmaceutical Medicine","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fe/ea/40290_2022_Article_432.PMC9334421.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40404370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}