The term In Silico Trial indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative.
Purpose: Staple line buttressing is a method of reinforcing surgical staple lines using buttress materials. This study evaluated surgical outcomes, hospital utilization, and hospital costs associated with staple line buttressing among patients who underwent primary laparoscopic sleeve gastrectomy (PLSG) in the United States. Methods: This was a retrospective cohort study using Premier Healthcare Database data from January 1, 2012 to December 31, 2017. Patients aged ≥ 18 years who underwent PLSG were selected and assigned to buttress or non-buttress cohorts based on the use of buttress material during their hospitalization for PLSG (index). Propensity score matching (PSM) was conducted to balance patient demographic and clinical characteristics between the cohorts. Generalized estimating equation models were used to compare the clinical and economic outcomes of the matched buttress and non-buttress users during the index hospitalization. Results: A total of 38,231 buttress and 27,349 non-buttress patients were included in the study. After PSM, 24,049 patients were retained in each cohort. Compared with non-buttress cohort, the buttress cohort patients had a similar rate of in-hospital leaks (0.28% vs 0.39%; p = 0.160) and a lower rate of bleeding (1.37% vs 1.80%, p = 0.015), transfusion (0.56% vs 0.77%, p = 0.050), and composite bleeding/transfusion (1.57% vs 2.04%, p = 0.019). Total costs ($12,201 vs $10,986, p < 0.001) and supply costs ($5366 vs $4320, p < 0.001) were higher in the buttress cohort compared with the non-buttress cohort. Conclusions: Staple line buttressing was associated with an improvement in complication rates for bleeding and transfusion. Total and supply costs were higher in the buttress cohort, necessitating further research into cost-effective buttressing materials.
Background: Chronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using computed tomography imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates. Research question: Can computed tomography imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns in order to better understand disease pathogenesis? Study design: and Methods: Using K-means clustering, we clustered participants from the COPDGene study (n=5273) based on CT imaging characteristics and then evaluated their clinical phenotypes. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n=360), and were further characterized using bronchial epithelial gene expression. Results: Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (TNF-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. Interpretation: Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the TNF-α and interferon-β pathways. Clinical trial registration: COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697).
Worldwide, the rehabilitation community has been impacted by the Corona Virus Disease 2019 (COVID-19). This impact has been disproportionately devastating for individuals with disabilities, and particularly individuals with acquired brain injury (ABI) due to injury-related cognitive and/or sensory/physical difficulties. Many physical and psychological symptoms of COVID-19 are already well known issues for individuals with ABI. Even in a fully functional social and healthcare system, post-ABI deficits can pose greater challenges to women and other marginalized groups, such as lesbian, gay, bisexual, transgender, gender-nonconforming, and queer/questioning-identified (LGBTQ+) individuals. The restrictions and changes brought about by COVID-19 have the potential to broaden the existing disparities and limitations. This commentary highlights three key areas to attend to during this pandemic to help assuage such disparities and limitations.
This paper is based on Helen Kiely’s Masters dissertation on MA in Library and Information Service Management, successfully completed at the University of Sheffield in 2018. The aim of the study was to explore the extent to which users of a health care library service understood common terminology used by clinical librarians/information professionals. A survey was developed based on the terminology used for common services and was distributed to staff and students at an acute NHS Foundation Trust. One hundred and eight people participated over a four week period and were asked to provide definitions to the terms. Analysis of the responses for accuracy and common themes indicates that jargon can be a barrier to user access and recommendations are made with respect to the need for outreach to users and the language used in this practice for creating better accessibility. F.J.
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Dynamic thin film interferometry is a technique used to non-invasively characterize the thickness of thin liquid films that are evolving in both space and time. Recovering the underlying thickness from the captured interferograms, unconditionally and automatically is still an open problem. Here we report a compact setup employing a snapshot hyperspectral camera and the related algorithms for the automated determination of thickness profiles of dynamic thin liquid films. The proposed technique is shown to recover film thickness profiles to within 100 nm of accuracy as compared to those profiles reconstructed through the manual color matching process. Subsequently, we discuss the characteristics and advantages of hyperspectral interferometry including the increased robustness against imaging noise as well as the ability to perform thickness reconstruction without considering the absolute light intensity information.
Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites. Materials and methods: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network. Results: On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC. Conclusions: ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.
This registry‐based study evaluated the contribution of center characteristics to kidney transplant outcomes in adult first kidney transplant recipients in Australia and New Zealand between 2004 and 2014. Primary outcomes were mortality and graft failure Secondary outcomes were transplant complications. Overall, 6970 transplants from 17 centers were included. For deceased donor transplants, 5‐year patient and graft survival rates varied considerably (81.0‐93.9% and 72.2‐88.3%, respectively). Variations in mortality and graft failure were partially reduced after adjustment for patient characteristics (1% and 20% reductions) and more markedly reduced after adjustment for center characteristics (41% and 55% reductions). For living donor transplants, 5‐year patient and graft survival rates varied (89.7‐100% and 79.2‐96.9%, respectively). Centers with high average total ischemic times (>14 hours) were associated with higher mortality for both deceased (adjusted hazard ratio [(AHR] 2.24, 95% CI 1.21 – 4.13) and living donor transplants (AHR 1.76, 95% CI 1.02 – 3.04). Small center size (<35 new kidney transplants/year) was associated with a lower hazard of mortality for living donor kidney transplants (AHR 0.48, 95% CI 0.28 – 0.81). No center characteristic was associated with graft failure. The appreciable variations in deceased donor kidney transplant recipient and graft survival outcomes across centers were attributable to center effects.
In late 2018, the Food and Drug Administration (FDA) outlined a framework for evaluating the possible use of real-world evidence (RWE) to support regulatory decision-making. This framework was created to facilitate studies that would generate high-quality RWE, including pragmatic clinical trials (PCTs), which are randomized trials designed to inform clinical or policy decisions by assessing the real-world effectiveness of an intervention. There is general agreement among experts that the use of existing healthcare and patient-generated data holds promise for making randomized trials more efficient, less costly, and more generalizable. Yet the benefits of relying on real-world data sources must be weighed against difficulties with ensuring data integrity and completeness. Additionally, appropriately monitoring patient safety in randomized trials of new drugs using healthcare system data that might not be available in real time can be quite difficult. Recognizing that these and other concerns are critical to the development and acceptability of PCTs, a group of stakeholders from academia, industry, professional organizations, regulatory bodies, government agencies, and patient advocates discussed a path forward for PCT growth and sustainability at a think tank meeting entitled “Monitoring and Analyzing Data from Pragmatic Streamlined Randomized Clinical Trials,” which took place in January 2019 (Washington, DC). The goals of this meeting were to: (1) evaluate study design and methodological options specific to PCTs that have the potential to yield high-quality evidence; (2) discuss best practices to ensure data quality in PCTs; and (3) identify appropriate methods for study monitoring. Proceedings from the think tank meeting are summarized in this manuscript.
Background: Mobile Health (mHealth) is becoming an important tool to improve health outcomes in maternal, newborn and child health (MNCH). Studies of mHealth interventions, have demonstrated their effectiveness in improving uptake of recommended maternal services such as antenatal visits. However, evidence of impact on maternal health outcomes is still limited. Methods: A pseudo-randomized controlled trial (single blind) was conducted to assess the impact of a voice-message based maternal intervention on maternal health knowledge, attitudes, practices and outcomes over time: Pregnancy (baseline/Time 1); Post-partum (Time 2) and when the infant turned one year old (Time 3). Women assigned to the mMitra intervention arm received gestational age- and stage-based educational voice messages via mobile phone in Hindi and Marathi, while those assigned to the control group did not. Both groups received standard care. Results: Two thousand sixteen women were enrolled. Interviews were conducted with 1516 women in the intervention group and 500 women in the control group at baseline and post-partum. The intervention group performed significantly better than controls on four maternal health practice indicators: receiving the tetanus toxoid injection (OR: 1.6, 95% Confidence Interval (CI): 1.05-2.4, p = 0.028), consulting a doctor if spotting or bleeding (OR: 1.72, 95%CI: 1.07-2.75, p = 0.025), saving money for delivery expenses (OR: 1.79, 95%CI: 1.38-2.33, p = 0.0001), and delivering in hospital (OR: 2.5, 95%CI: 1.49-4.35, p = 0.001). The control group performed significantly better than the intervention group on two practice indicators: resting regularly during pregnancy (OR: 0.7, 95%CI: 0.54-0.88, p = 0.002) and having at-home deliveries attended by a skilled birth attendant (OR: 0.46, 95%CI: 0.23-0.91, p = 0.027). Both groups' knowledge improved from Time 1 to Time 2. Only one knowledge indicator, on seeking medical care during pregnancy, was statistically increased in the intervention group compared to controls. Anemia status at or near the time of delivery was unable to be assessed due to missing data from maternal health cards. Conclusions: This study provides evidence that in low-resource settings, mobile voice messages providing tailored and timed information about pregnancy can positively impact maternal health care practices proven to improve maternal health outcomes. Additional research is needed to assess whether voice messaging can motivate behavior change better than text messaging, particularly in low literacy settings. Trial registration: The mMitra impact evaluation is registered with ISRCTN under Registration # 88968111, assigned on 6 September 2018 (See https://www.isrctn.com/ISRCTN88968111).
Post-approval changes are inevitable and necessary throughout the life of a drug product-to implement new knowledge, maintain a state of control, and drive continual improvement. Many post-approval changes require regulatory agency approval by individual countries before implementation. Because of the global regulatory complexity, individual post-approval changes usually take years for full worldwide approval even when they reduce patient risk, improve compliance, or enhance the manufacturing process or test methods. This global complexity slows down continual improvement and innovation and can cause drug shortages and current good manufacturing practices compliance issues. Manufacturers that market products globally experience the greatest challenge and risks in their daily operations because of this post-approval change complexity. A global problem needs a global solution. This paper has been sponsored and endorsed by senior Quality leaders (Chief Quality Officers and Heads of Quality) from more than 25 global pharmaceutical companies who have collaborated to speak with ″One-Voice-Of-Quality″ (1VQ). The paper provides two solutions that lay the foundation for an aligned and standardized industry position on the topic of effective management of post-approval changes in the Pharmaceutical Quality System (PQS). This document represents the 1VQ standard approach for the steps necessary to establish and demonstrate an effective quality system to fully leverage a risk-based approach to post-approval changes as laid out by ICH Q10 Annex 1. Implementation of the solutions presented in this paper can help achieve a transformational shift with faster implementation of new knowledge, continual improvement, and innovation through post-approval changes.
This article reviews currently available scientific literature related to the epidemiology, infectivity, survival, and susceptibility to disinfectants of Coronaviruses, in the context of the controls established to meet Good Manufacturing Practice regulations and guidance, and the public health guidance issued specifically to combat the Covid-19 pandemic. The possible impact of the COVID-19 pandemic on the pharmaceutical supply chain is assessed and recommendations are listed for risk mitigation steps to minimize supply disruption to pharmaceutical drug products. Areas addressed include a brief history of the COVID-19 viral pandemic, a description of the virus, the regulatory response to the pandemic, the screening of employees, the persistence on inanimate surfaces, cleaning and disinfection of manufacturing facilities, use of GMP-mandated personal protective equipment to counter the spread of the disease, the role of air changes in viral clearance, approaches to risk assessment and mitigation. Biological medicinal products have a great record of safety, yet the cell cultures used for production can be susceptible to viruses, and contamination events have occurred. Studies on SARS-CoV-1 for its ability to replicate in various mammalian cell lines used for biopharmaceutical manufacturing suggest SARS-CoV-2 poses low risk and any contamination would be detected by currently used adventitious virus testing. The consequences of the potential virus exposure of manufacturing processes, as well as the effectiveness of mitigation efforts are discussed. The pharmaceutical supply chain is complex, traversing many geographies and companies that range from large multinationals to mid and small size operations. This paper recommends practices that can be adopted by all companies, irrespective of their size, geographic location, or position in the supply chain.
EGFR exon 20 insertion driver mutations (Exon20ins) in NSCLC are insensitive to EGFR-TKIs. Amivantamab (JNJ-61186372), a bispecific antibody targeting EGFR/cMet, has shown preclinical activity in TKI-sensitive EGFR-mutated NSCLC models and in an ongoing first-in-human study in advanced NSCLC patients. However, the activity of amivantamab in Exon20ins-driven tumors has not yet been described. Ba/F3 cells and patient-derived cells/organoids/xenograft models harboring diverse Exon20ins were used to characterize the antitumor mechanism of amivantamab. Amivantamab inhibited proliferation by effectively downmodulating EGFR/cMet levels and inducing immune-directed antitumor activity with increased IFN-γ secretion in various models. Importantly, in vivo efficacy of amivantamab was superior to cetuximab or poziotinib, an experimental Exon20ins targeted-TKI. Amivantamab produced robust tumor responses in two Exon20ins patients, highlighting the important translational nature of this preclinical work. These findings provide mechanistic insight into the activity of amivantamab and support its continued clinical development in Exon20ins patients, an area of high unmet medical need.
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