Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments.

Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.

A Cancer Education Framework for Australian Medical Schools: an Announcement of a New Educational Program.

This framework draws upon national and international cancer curricula to identify the essential cancer-related learning outcomes for Australian medical students. The framework incorporates feedback from medical, radiation and surgical oncologists, haematologists, and palliative care physicians on what medical graduates need to know about cancer. The consensus view was that medical students require a basic understanding of the principles of cancer management and the opportunity to see cancer patients in a cancer service unit. The framework assumes that certain knowledge, skills, and attitudes are already embedded in current Australian medical school curricula, presenting instead only the core cancer content in order to provide a clear and concise framework designed to maximise integration within existing curricula.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.

A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling.

This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

International Consultation on Incontinence 2016; Executive summary: Urodynamic testing.

AIMS: The International Consultation on Incontinence has published an update of the recommendations for the diagnosis and management of urine incontinence (ICI2016). This manuscript summarizes the consultations committee-recommendations with regard to urodynamic assessment. METHODS: Expert consensus on the basis of structured evidence assessment has been the basis of the consultations publication and has been summarized by the committee for this manuscript. RESULTS: Patients that are not satisfied with their initial management on the basis of their reported signs and symptoms of urinary incontinence, as well as all patients with neurological abnormalities that are potentially relevant for the function of the lower urinary tract, may very likely profit from objective diagnosis and staging and grading of their dysfunction, with urodynamic testing, regardless their age, vulnerability and/or comorbidities. The principles and technical innovations as well as the principal recommendations for the utilization of (invasive) urodynamic assessment for women, men, children, and vulnerable elderly, with or without neurogenic lower urinary tract dysfunction with urinary incontinence are provided in this abbreviated ICI recommendations-document. CONCLUSIONS: The ICI2016 committee on urodynamics presents an executive summary of the most important reasons and recommendations for the use of urodynamic investigations for patients with urinary incontinence.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

Sonographers' self-reported visualization of normal postmenopausal ovaries on transvaginal ultrasound is not reliable: results of expert review of archived images from UKCTOCS.

OBJECTIVE: In the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), self-reported visualization rate (VR) of the ovaries by the sonographer on annual transvaginal sonographic (TVS) examinations was a key quality control (QC) metric. The objective of this study was to assess self-reported VR using expert review of a random sample of archived images of TVS examinations from UKCTOCS, and then to develop software for measuring VR automatically. METHODS: A single expert reviewed images archived from 1000 TVS examinations selected randomly from 68 931 TVS scans performed in UKCTOCS between 2008 and 2011 with ovaries reported as 'seen and normal'. Software was developed to identify the exact images used by the sonographer to measure the ovaries. This was achieved by measuring caliper dimensions in the image and matching them to those recorded by the sonographer. A logistic regression classifier to determine visualization was trained and validated using ovarian dimensions and visualization data reported by the expert. RESULTS: The expert reviewer confirmed visualization of both ovaries (VR-Both) in 50.2% (502/1000) of the examinations. The software identified the measurement image in 534 exams, which were split 2:1:1 providing training, validation and test data. Classifier mean accuracy on validation data was 70.9% (95% CI, 70.0-71.8%). Analysis of test data (133 exams) provided a sensitivity of 90.5% (95% CI, 80.9-95.8%) and specificity of 47.5% (95% CI, 34.5-60.8%) in detecting expert confirmed visualization of both ovaries. CONCLUSIONS: Our results suggest that, in a significant proportion of TVS annual screens, the sonographers may have mistaken other structures for normal ovaries. It is uncertain whether or not this affected the sensitivity and stage at detection of ovarian cancer in the ultrasound arm of UKCTOCS, but we conclude that QC metrics based on self-reported visualization of normal ovaries are unreliable. The classifier shows some potential for addressing this problem, though further research is needed. © 2017 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

In silico toxicology protocols.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Preconception counselling for low health literate women: an exploration of determinants in the Netherlands.

BACKGROUND: Women from lower socioeconomic groups tend to be at greater risk of adverse perinatal outcomes, but are less likely to participate in preconception counselling compared to higher socioeconomic groups. This could be partly because of their limited skills to assess, understand and use health related information in ways that promote and maintain good health (health literacy skills). In this study we explored determinants of participation in preconception counselling among women with low health literacy in The Netherlands. METHODS: Potential determinants of participation in preconception counselling were derived from the literature, and mapped onto a theoretical framework, which was tested for perceived relevance and completeness in an expert review (n = 20). The framework was used to prepare face-to-face interviews with women with low health literacy and a wish to conceive (n = 139). In the interviews we explored preconception counselling awareness, knowledge, considerations, subjective norms, self-efficacy, attitude, and intention. Linear regression analyses were used to test associations with intention to participate in preconception counselling. RESULTS: Most women (75%) were unaware of the concept of preconception counselling and the provision of counselling, even if they lived in areas where written invitations had been disseminated. Common considerations for participation were: preparation for pregnancy; perceived lack of information; and problems in a previous pregnancy. Considerations not to participate were mostly related to perceived sufficient knowledge and perceived low risk of perinatal problems. Respondents generally had a positive attitude towards participation in preconception counselling for themselves, and 41% reported that they would participate in preconception counselling. CONCLUSION: Women with low health literacy were generally unaware of the concept and provision of preconception counselling, but seemed to be interested in participation. Further research should investigate how to effectively reach and inform this group about preconception counselling. This knowledge is essential for evidence-based development of interventions to increase the accessibility and understanding of preconception counselling.

A consortium-driven framework to guide the implementation of ICH M7 Option 4 control strategies.

The ICH M7 Option 4 control of (potentially) mutagenic impurities is based on the use of scientific principles in lieu of routine analytical testing. This approach can reduce the burden of analytical testing without compromising patient safety, provided a scientifically rigorous approach is taken which is backed up by sufficient theoretical and/or analytical data. This paper introduces a consortium-led initiative and offers a proposal on the supporting evidence that could be presented in regulatory submissions.

Utility of published DNA reactivity alerts.

The identification of impurities with mutagenic potential is required for any potential pharmaceutical. The ICH M7 guidelines state that two complementary in silico toxicity prediction tools may be used to predict the mutagenic potential of pharmaceutical impurities. An expert review of the resulting in silico predictions is required, and numerous publications have been released to guide the expert review process. One such publication suggests that literature-based structural alerts (LBSAs) may provide a suitable aid in the expert review process. This publication provides a study of the effect of using one such set of LBSAs for the expert review of mutagenicity predictions from two complementary in silico tools. The analysis was performed using an Ames test dataset of 2619 compounds, and required interpretation of the LBSAs which proved to be a subjective process. Globally the LBSAs produced many more false positives than the in silico systems; whilst some exhibited a predictive performance comparable to the in silico systems, the majority were overly sensitive at the cost of accuracy. Use of LBSAs as part of an expert review process, without considering mitigating factors, could result in many more false positives and potentially the need to carry out additional and unnecessary Ames tests.

Carbamates and ICH M7 classification: Making use of expert knowledge.

Carbamates are widely used in the chemical industry so understanding their toxicity is important to safety assessment. Carbamates have been associated with certain toxicities resulting in publication of structural alerts, including alerts for mutagenicity. Structural alerts for bacterial mutagenicity can be used in combination with statistical systems to enable ICH M7 classification, which allows assessment of the genotoxic risk posed by pharmaceutical impurities. This study tested a hypothetical bacterial mutagenicity alert for carbamates and examined the impact it would have on ICH M7 classifications using (Q)SAR predictions from the expert rule-based system Derek Nexus and the statistical-based system Sarah Nexus. Public datasets have a low prevalence of mutagenic carbamates, which highlighted that systems containing an alert for carbamates perform poorly for achieving correct ICH M7 classifications. Carbamates are commonly used as protecting groups and proprietary datasets containing such compounds were also found to have a low prevalence of mutagenic compounds. Expert review of the mutagenic compounds established that mutagenicity was often only observed under certain (non-standard) conditions and more generally that the Ames test may be a poor predictor for the risk of carcinogenicity posed by chemicals in this class. Overall a structural alert for the in vitro bacterial mutagenesis of carbamates does not benefit workflows for assigning ICH M7 classification to impurities.

Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses.

The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.

Developing a systematic approach to safer medication use during pregnancy: summary of a Centers for Disease Control and Prevention--convened meeting.

To address information gaps that limit informed clinical decisions on medication use in pregnancy, the Centers for Disease Control and Prevention (CDC) solicited expert input on a draft prototype outlining a systematic approach to evaluating the quality and strength of existing evidence for associated risks. The draft prototype outlined a process for the systematic review of available evidence and deliberations by a panel of experts to inform clinical decision making for managing health conditions in pregnancy. At an expert meeting convened by the CDC in January 2013, participants divided into working groups discussed decision points within the prototype. This report summarizes their discussions of best practices for formulating an expert review process, developing evidence summaries and treatment guidance, and disseminating information. There is clear recognition of current knowledge gaps and a strong collaboration of federal partners, academic experts, and professional organizations willing to work together toward safer medication use during pregnancy.

Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older.

BACKGROUND: A multitude of mhealth (mobile health) apps have been developed in recent years to support effective self-management of patients with diabetes mellitus type 1 or 2. OBJECTIVE: We carried out a systematic review of all currently available diabetes apps for the operating systems iOS and Android. We considered the number of newly released diabetes apps, range of functions, target user groups, languages, acquisition costs, user ratings, available interfaces, and the connection between acquisition costs and user ratings. Additionally, we examined whether the available applications serve the special needs of diabetes patients aged 50 or older by performing an expert-based usability evaluation. METHODS: We identified relevant keywords, comparative categories, and their specifications. Subsequently, we performed the app review based on the information given in the Google Play Store, the Apple App Store, and the apps themselves. In addition, we carried out an expert-based usability evaluation based on a representative 10% sample of diabetes apps. RESULTS: In total, we analyzed 656 apps finding that 355 (54.1%) offered just one function and 348 (53.0%) provided a documentation function. The dominating app language was English (85.4%, 560/656), patients represented the main user group (96.0%, 630/656), and the analysis of the costs revealed a trend toward free apps (53.7%, 352/656). The median price of paid apps was €1.90. The average user rating was 3.6 stars (maximum 5). Our analyses indicated no clear differences in the user rating between free and paid apps. Only 30 (4.6%) of the 656 available diabetes apps offered an interface to a measurement device. We evaluated 66 apps within the usability evaluation. On average, apps were rated best regarding the criterion "comprehensibility" (4.0 out of 5.0), while showing a lack of "fault tolerance" (2.8 out of 5.0). Of the 66 apps, 48 (72.7%) offered the ability to read the screen content aloud. The number of functions was significantly negative correlated with usability. The presence of documentation and analysis functions reduced the usability score significantly by 0.36 and 0.21 points. CONCLUSIONS: A vast number of diabetes apps already exist, but the majority offer similar functionalities and combine only one to two functions in one app. Patients and physicians alike should be involved in the app development process to a greater extent. We expect that the data transmission of health parameters to physicians will gain more importance in future applications. The usability of diabetes apps for patients aged 50 or older was moderate to good. But this result applied mainly to apps offering a small range of functions. Multifunctional apps performed considerably worse in terms of usability. Moreover, the presence of a documentation or analysis function resulted in significantly lower usability scores. The operability of accessibility features for diabetes apps was quite limited, except for the feature "screen reader".

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer.

PURPOSE: The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer. METHODS: A DL model was developed for predicting patient-specific doses, trained and validated on a dataset of 235 patients with diverse target volumes and prescriptions. The model was integrated into clinical workflow with DL-predicted objective functions. The automatic plans were retrospectively designed for additional 50 treated manual volumetric modulated arc therapy (VMAT) plans. A comparison was made between automatic and manual plans in terms of dosimetric indexes, monitor units (MUs) and planning time. Plan quality metric (PQM) encompassing these indexes was evaluated, with higher PQM values indicating superior plan quality. Qualitative evaluations of two plans were conducted by four reviewers. RESULTS: The PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans (P = 0.75). Compared to manual plans, the targets coverage and homogeneity of automatic plans demonstrated no significant difference. Manual plans exhibited better sparing for lung in V5 (difference: 1.8 ± 4.2 %, P = 0.02), whereas automatic plans showed enhanced sparing for heart in V30 (difference: 1.4 ± 4.7 %, P = 0.02) and for spinal cord in Dmax (difference: 0.7 ± 4.7 Gy, P = 0.04). The planning time and MUs of automatic plans were significantly reduced by 70.5 ± 20.0 min and 97.4 ± 82.1. Automatic plans were deemed acceptable in 88 % of the reviews (176/200). CONCLUSIONS: The DL-assisted approach for lung cancer notably decreased planning time and MUs, while demonstrating comparable or superior quality relative to manual plans. It has the potential to provide benefit to lung cancer patients.

Hybrid atrial fibrillation ablation.

In this state-of-the art review on hybrid atrial fibrillation (AF) ablation, we briefly focus on the pathophysiology of AF, the rationale for the hybrid approach, its technical aspects and the efficacy and safety outcomes after hybrid AF ablation, both from meta-analyses and randomized control trial data. Also, we performed a systematic search to provide a provisional overview of real-world hybrid AF ablation efficacy and safety outcomes. Furthermore, we give an insight into the 'Maastricht approach', an approach that allows us to tailor the ablation procedure to the individual patient. Finally, we reflect on future perspectives with the objective to continue improving our thoracoscopic hybrid AF ablation approach. Based on the review of the available literature, we believe it is fair to state that thoracoscopic hybrid AF ablation is a valid alternative to catheter ablation for the treatment of patients with more persistent forms of AF.

Designing sustainable mobile weight management applications: information technology (IT) experts perspectives.

Background: Weight management mobile applications have become increasingly popular as a tool for individuals to achieve their weight loss goals. However, key challenges with mobile weight management applications are sustaining user engagement and promoting long-term behavior change. This study examines the key functions and features of weight management mobile applications from the viewpoint of information technology (IT) experts. We have developed a framework that outlines the optimal design strategies for mobile apps to support sustainable weight management. Methods: We conducted face-to-face interviews with five experts to evaluate and give input on the proposed framework. Results: Eight specialized functionalities have been categorized and defined based on initial framework system features and recommended features based on experts' feedback: (I) customization; (II) usability; (III) integration; (IV) online support; (V) tracking; (VI) nutrition database; (VII) gamification; and (VIII) notification. In addition, common functionalities were identified as (I) registration/sign in; (II) data privacy; (III) security; and (IV) user feedback. An online discussion indicates the system functionality in four themes: (I) configuration; (II) data analytics; (III) support/performance; and (IV) platform: iOS, Android, Native. Conclusions: These functionalities and features will be incorporated into a framework to design mobile applications for sustainable weight management. Also, to be part of user interface development for the user experience experiments.

Emerging therapies for opioid-induced constipation: what can we expect?

INTRODUCTION: The rise in opioid use for managing chronic and oncologic pain has led to a significant increase in opioid-induced constipation (OIC) that impacts patient quality of life and pain management. AREAS COVERED: In this study, emerging therapies for OIC were criticized for refining advancements and novel treatment options. Key topics included the efficacy of peripherally acting mu-opioid receptor antagonists (PAMORAs) such as methylnaltrexone, naloxegol, and naldemedine, which specifically target opioid-induced gut dysfunction. Other treatment options, including intestinal secretagogues like lubiprostone and linaclotide, selective 5-HT receptor agonists such as prucalopride, and emerging adjunctive therapies like transcutaneous electrical nerve stimulation (TENS) and electroacupuncture were mentioned. Current guidelines from the American Gastroenterological Association (AGA) and the European consensus were criticized. EXPERT OPINION: Experts stress the importance of a stepwise approach to managing OIC, considering patient-specific factors and the efficacy of various treatments. While PAMORAs have demonstrated effectiveness in improving bowel function, their high cost and lack of extensive head-to-head comparisons with traditional laxatives are significant concerns. Emerging therapies and adjunctive treatments offer promising results but require further validation through rigorous studies. Future research should focus on long-term outcomes, cost-effectiveness, and comparative effectiveness to better address the complex needs of patients with OIC and refine treatment protocols.