The global pharmaceutical industry portfolio is skewed towards cancer and rare diseases due to more predictable development pathways and financial incentives. In contrast, drug development for major chronic health conditions that are responsible for a large part of mortality and disability worldwide is stalled. To examine the processes of novel drug development for common chronic health conditions, a multistakeholder Think Tank meeting, including thought leaders from academia, clinical practice, non-profit healthcare organizations, the pharmaceutical industry, the Food and Drug Administration (FDA), payors as well as investors, was convened in July 2022. Herein, we summarize the proceedings of this meeting, including an overview of the current state of drug development for chronic health conditions and key barriers that were identified. Six major action items were formulated to accelerate drug development for chronic diseases, with a focus on improving the efficiency of clinical trials and rapid implementation of evidence into clinical practice: 1. Involve implementation science in the early phases of drug development. 2. Involve regulatory agencies early in drug development, simplify clinical trial conduct and improve inter -agency collaboration. 3. Involve payors early in drug development. 4. Investigate novel implementation strategies. 5. Increase focus on and funding for implementation science to develop strategies that improve utilization of proven effective treatments. 6. Change public perception.

BACKGROUND:Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described.METHODS:We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance.RESULTS:Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials.CONCLUSION:We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges.

Although the development of therapies and tools for the improved management of heart failure (HF) continues apace, day-to-day management in clinical practice is often far from ideal. A Cardiovascular Round Table workshop was convened by the European Society of Cardiology (ESC) to identify barriers to the optimal implementation of therapies and guidelines and to consider mitigation strategies to improve patient outcomes in the future. Key challenges identified included the complexity of HF itself and its treatment, financial constraints and the perception of HF treatments as costly, failure to meet the needs of patients, suboptimal outpatient management, and the fragmented nature of healthcare systems. It was discussed that ongoing initiatives may help to address some of these barriers, such as changes incorporated into the 2021 ESC HF guideline, ESC Heart Failure Association quality indicators, quality improvement registries (e.g. EuroHeart), new ESC guidelines for patients, and the universal definition of HF. Additional priority action points discussed to promote further improvements included revised definitions of HF 'phenotypes' based on trial data, the development of implementation strategies, improved affordability, greater regulator/payer involvement, increased patient education, further development of patient-reported outcomes, better incorporation of guidelines into primary care systems, and targeted education for primary care practitioners. Finally, it was concluded that overarching changes are needed to improve current HF care models, such as the development of a standardized pathway, with a common adaptable digital backbone, decision-making support, and data integration, to ensure that the model 'learns' as the management of HF continues to evolve.

Introduction: Clinical Endpoint Adjudication is a critical component of Cardiovascular (CV) clinical outcome trials and relies on manual decision making to adjudicate clinical events, including CV death. This process is resource intensive and subject to human-driven variance. As part of a broader initiative to automate data collection and analysis of clinical endpoints, we evaluated whether machine learning (ML) algorithms could replicate the outcomes of expert adjudication for CV death. Algorithms were trained on data from THEMIS (NCT01991795), a large, randomized trial comparing ticagrelor to placebo in patients with Type 2 Diabetes Mellitus. Methods: We deployed a deep learning, biomedical named entity (NE) extraction model called BERN to extract relevant NE’s from clinical text of THEMIS events. NE’s were transformed into a sparse, numerical matrix and concatenated with event-level structured features to form a data set of 962 events. Expert adjudicator consensus for CV death was used as ground truth. We trained models using grid search and cross validation, experimenting with XGBoost, Random Forest, Logistic Regression, and Naïve Bayes. Performance was assessed on a 25% (240 of 962) validation subset of the data excluded from training. Metrics used to evaluate performance were Precision, Recall, Accuracy and Area Under the Receiver Operating Characteristic Curve (AUC). Results: Best performance was observed on models trained using Naïve Bayes (>97% ROC AUC on validation data), see Table. Top ranked features for classifying CV death included site investigator decision, sex, and NE’s associated with diagnoses or symptoms, such as “edema” and “chest pain.” Conclusion: With high consistency between automated and expert adjudication, our models demonstrate that machine learning may augment or even replace clinician adjudication in CV outcome trials. Subsequent research will focus on pursuing ML approaches to adjudicate other outcome events.

Abstract Background and introduction Accurate identification of clinical outcome events is critical to obtaining reliable results in cardiovascular outcomes trials (CVOTs). Current processes for event adjudication are expensive and hampered by delays. As part of a larger project to more reliably identify outcomes, we evaluated the use of machine learning to automate event adjudication using data from the SOCRATES trial (NCT01994720), a large randomized trial comparing ticagrelor and aspirin in reducing risk of major cardiovascular events after acute ischemic stroke or transient ischemic attack (TIA). Purpose We studied whether machine learning algorithms could replicate the outcome of the expert adjudication process for clinical events of ischemic stroke and TIA. Could classification models be trained on historical CVOT data and demonstrate performance comparable to human adjudicators? Methods Using data from the SOCRATES trial, multiple machine learning algorithms were tested using grid search and cross validation. Models tested included Support Vector Machines, Random Forest and XGBoost. Performance was assessed on a validation subset of the adjudication data not used for training or testing in model development. Metrics used to evaluate model performance were Receiver Operating Characteristic (ROC), Matthews Correlation Coefficient, Precision and Recall. The contribution of features, attributes of data used by the algorithm as it is trained to classify an event, that contributed to a classification were examined using both Mutual Information and Recursive Feature Elimination. Results Classification models were trained on historical CVOT data using adjudicator consensus decision as the ground truth. Best performance was observed on models trained to classify ischemic stroke (ROC 0.95) and TIA (ROC 0.97). Top ranked features that contributed to classification of Ischemic Stroke or TIA corresponded to site investigator decision or variables used to define the event in the trial charter, such as duration of symptoms. Model performance was comparable across the different machine learning algorithms tested with XGBoost demonstrating the best ROC on the validation set for correctly classifying both stroke and TIA. Conclusions Our results indicate that machine learning may augment or even replace clinician adjudication in clinical trials, with potential to gain efficiencies, speed up clinical development, and retain reliability. Our current models demonstrate good performance at binary classification of ischemic stroke and TIA within a single CVOT with high consistency and accuracy between automated and clinician adjudication. Further work will focus on harmonizing features between multiple historical clinical trials and training models to classify several different endpoint events across trials. Our aim is to utilize these clinical trial datasets to optimize the delivery of CVOTs in further cardiovascular drug development. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): AstraZenca Plc

BACKGROUND:Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum.RESULTS:Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas.CONCLUSIONS:ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.

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.

Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.

Background and Purpose: Data monitoring committees are responsible for safeguarding the interests of study participants and assuring the integrity and credibility of clinical trials. The independence of data monitoring committees from sponsors and investigators is essential in achieving this mission. Creative approaches are needed to address ongoing and emerging challenges that potentially threaten data monitoring committees’ independence and effectiveness. Methods: An expert panel of representatives from academia, industry and government sponsors, and regulatory agencies discussed these challenges and proposed best practices and operating principles for effective functioning of contemporary data monitoring committees. Results and Conclusions: Prospective data monitoring committee members need better training. Options could include didactic instruction as well as apprenticeships to provide real-world experience. Data monitoring committee members should be protected against legal liability arising from their service. While avoiding breaches in confidentiality of interim data remains a high priority, data monitoring committees should have access to unblinded efficacy and safety data throughout the trial to enable informed judgments about risks and benefits. Because overly rigid procedures can compromise their independence, data monitoring committees should have the flexibility necessary to best fulfill their responsibilities. Data monitoring committee charters should articulate principles that guide the data monitoring committee process rather than list a rigid set of requirements. Data monitoring committees should develop their recommendations by consensus rather than through voting processes. The format for the meetings of the data monitoring committee should maintain the committee’s independence and clearly establish the leadership of the data monitoring committee chair. The independent statistical group at the Statistical Data Analysis Center should have sufficient depth of knowledge about the study at hand and experience with trials in general to ensure that the data monitoring committee has access to timely, reliable, and readily interpretable insights about emerging evidence in the clinical trial. Contracts engaging data monitoring committee members for industry-sponsored trials should have language customized to the unique responsibilities of data monitoring committee members rather than use language appropriate to consultants for product development. Regulatory scientists would benefit from experiencing data monitoring committee service that does not conflict with their regulatory responsibilities.

OBJECTIVE:The population pharmacokinetics of ticagrelor and its active metabolite AR-C124910XX were characterized following ticagrelor 60 mg or 90 mg twice daily oral long-term treatment in 4,426 patients with a history of myocardial infarction.METHODS:The ticagrelor and AR-C124910XX plasma concentration-time data were described by one-compartment models with first-order absorption or metabolite formation and elimination.RESULTS:Systemic exposure to ticagrelor and AR-C124910XX were stable over time. Ticagrelor apparent clearance (CL/F) was 17 L/h for the 60-mg and 15.4 L/h for the 90-mg dose. The CL/F of AR-C124910XX was 11.1 L/h for the 60-mg and 9.95 L/h for the 90-mg dose. Both ticagrelor and AR-C124910XX CL/F were independently influenced by body weight, sex, age, smoking, and Japanese ethnicity. Female sex and age > 75 years were the only categorical covariates, having more than 20% effect on AR-C124910XX CL/F. Ticagrelor CL/F was 6% higher and 11% lower, whereas AR-C124910XX CL/F was 26% higher and 34% lower for patients weighing 110 and 50 kg, respectively, compared with an 83 kg patient.CONCLUSIONS:The small differences in exposure to both ticagrelor and AR-C124910XX between demographic subgroups were in accordance with the consistent efficacy and safety outcomes observed across the population. The results were similar to those observed previously in patients with acute coronary syndromes.
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Background: Antiplatelet agents increase bleeding risk. Few data on hemostatic benefits of platelet transfusion exist. Objective: To assess the effect of autologous platelet transfusion on ticagrelor-mediated and clopidogrel-mediated platelet inhibition in a single-center, open-label, randomized, cross-over study (NCT01744288). Methods: Forty-four healthy subjects received ticagrelor (180 mg) or clopidogrel (600 mg; two functional CYP2C19 alleles [*1 or *17] required) with or without platelet transfusion (14-day washout). Subjects received one autologous platelet apheresis unit (approximately six pooled donor platelet units) 24 h (n = 15) or 48 h (n = 13) after ticagrelor or 48 h after clopidogrel (n = 16). Platelet apheresis was conducted 72 h before transfusion. Aspirin (81 mg per day) was taken from after apheresis until 24 h before transfusion. P2Y12 reaction units (PRUs) and inhibition of platelet aggregation (IPA) induced by ADP were measured. Results: Mean age and body mass index were 30 years (standard deviation [SD] 6 years) and 26.9 kg m(-2) (SD 4.0 kg m(-2)), respectively; 98% of subjects were men, and 39 of 44 completed treatment. Platelet transfusion 24 h after ticagrelor had minimal effects on IPA or PRU values within 48 h after transfusion. Platelet transfusion 48 h after ticagrelor also had minimal effects on IPA or PRU values at most post-transfusion times. Platelet transfusion 48 h after clopidogrel, versus no transfusion, had a small reversing effect on IPA (24 h, 36 h, and 48 h) and PRU values (12 h, 24 h, and 36 h) after transfusion. Conclusions: Autologous platelet transfusion is unlikely to be of clinical benefit in reversing the antiplatelet effects of ticagrelor. The clinical relevance of the small effects seen with clopidogrel is unknown.

The relationships between drug exposure and the composite risk of cardiovascular (CV) death, myocardial infarction (MI), and stroke as well as the risk of TIMI major bleeding were estimated following long-term treatment with ticagrelor 60 or 90 mg twice daily in 20,942 patients with prior MI. These analyses support the primary reported efficacy and safety evaluations by showing that there were clear separations from placebo early in treatment with both doses, regardless of ticagrelor exposure, for both endpoints. In addition, the exposure-response analyses provided new insight into the contribution of individual exposure levels, rather than dose, as a predictor of events and accounted for differences in the baseline risk between patients. The predicted risks of CV death/MI/stroke were similar despite an increase in the median predicted ticagrelor average steady-state concentration from 606 nmol/L with ticagrelor 60 mg to 998 nmol/L with ticagrelor 90 mg (hazard ratios vs placebo of 0.83 and 0.81, respectively). The corresponding predicted risk of TIMI major bleeding slightly increased (hazard ratios vs placebo of 2.4 and 2.6, respectively). Apart from Japanese patients, showing a lower risk of CV death/MI/stroke, the response to ticagrelor was consistent across the study population, as supported by the combination of relatively flat exposure-response relationships in the studied exposure range, similar sensitivity to ticagrelor exposure, and small exposure differences. Consequently, the present analyses support the selection of the 60-mg dose for all demographic subgroups of patients studied.

Divergent strategies have emerged for the management of severe asthma. One strategy utilises high and fixed doses of maintenance treatment, usually inhaled corticosteroid/long-acting β2-agonist (ICS/LABA), supplemented by a short-acting β2-agonist (SABA) as needed. Alternatively, budesonide/formoterol is used as both maintenance and reliever therapy. The latter is superior to fixed-dose treatment in reducing severe exacerbations while achieving similar or better asthma control in other regards. Exacerbations may be reduced by the use of budesonide/formoterol as reliever medication during periods of unstable asthma. We examined the risk of a severe exacerbation in the period after a single day with high reliever use.

Background: Concerns exist that regular long-acting beta(2)-adrenergic agonist (LABA) therapy may increase the risk of serious asthma-related events. Objective: To assess risks of formoterol-containing versus non-LABA treatment by using a large asthma database.Methods: This analysis included all blind, parallel-arm, randomized, active-controlled and/or placebo-controlled AstraZeneca-sponsored asthma studies with formoterol-containing and non-LABA comparator arms. Serious adverse events were assessed for inclusion in all-cause death, asthma-related death, asthma-related intubation, and asthma-related hospitalization categories by using blind adjudication. Data were combined across trials; relative risk (RR) was assessed by using Mantel-Haenszel methods.Results: Data were from 13,542 formoterol-randomized and 9968 non-LABA patients 4 years or older (42 trials), of whom 93% and 89%, respectively, received inhaled corticosteroid as part of randomized treatment or allowed medication. Incidence of all-cause death was low (n = 3 and n = 4, respectively), with numerically lower all-cause deaths/1000 patient-treatment years in the formoterol-treated group (0.53) versus the non-LABA group (0.82) (RR, 0.64; 95% confidence interval [CI], 0.14-2.92). No asthma-related deaths and 1 asthma-related intubation (formoterol-treated group) occurred. Asthma-related hospitalizations/1000 patient-treatment years were lower numerically in the formoterol-treated group (12.1) versus the non-LABA group (16.4) (RR, 0.73; 95% CI, 0.54-1.01), with fewer study discontinuations in the formoterol-treated group (12.7% vs 15.4%, respectively; RR, 0.79; 95% CI, 0.74-0.85). Relative to non-LABA, increasing daily formoterol dose (>= 4.5, 9, 18, 36 mu g) did not increase the rate or incidence of asthma-related hospitalization.Conclusion: No evidence of increased risk of asthma-related hospitalization, no asthma-related deaths, and a low incidence of all-cause death and asthma-related intubation were seen with formoterol-containing versus non-LABA treatment. (J Allergy Clin Immunol 2010;125:390-6.)

The Global Initiative for Asthma (GINA) guidelines aim at improving asthma control and preventing future risk. For patients with moderate to severe asthma an inhaled corticosteroid (ICS) or an ICS/long-acting beta2-agonist (LABA) combination with a short-acting beta2-agonist (SABA) as reliever is recommended. Despite the availability of effective maintenance therapies, a large proportion of patients still fail to achieve guideline-defined asthma control, and overuse of SABA reliever medication at the expense of ICS is commonly observed. New simplified treatment approaches may offer a solution and assist physicians to achieve overall asthma control. One such treatment approach, which is recommended in the GINA guidelines, is budesonide/formoterol for both maintenance and reliever therapy. This treatment strategy significantly reduces the rate of severe asthma exacerbations compared with ICS/LABA plus SABA and achieves equivalent daily symptom control compared with higher doses of ICS/LABA plus separate SABA for relief. These benefits are achieved at a lower overall steroid load, and budesonide/formoterol maintenance and reliever therapy is well tolerated in patients with moderate to severe asthma. This review discusses current asthma management in patients with moderate to severe disease and examines the evidence for alternative asthma management approaches.

1 The endocannabinoid anandamide is an emerging potential signalling molecule in the cardiovascular system. Anandamide causes vasodilatation, bradycardia and hypotension in animals and has been implicated in the pathophysiology of endotoxic, haemorrhagic and cardiogenic shock, but its vascular effects have not been studied in man.2 Human forearm blood flow and skin microcirculatory flow were recorded using venous occlusion plethysmography and laser-Doppler perfusion imaging ( LDPI), respectively. Each test drug was infused into the brachial artery or applied topically on the skin followed by a standardized pin-prick to disrupt the epidermal barrier.3 Anandamide failed to affect forearm blood flow when administered intra-arterially at infusion rates of 0.3-300 nmol min(-1). The highest infusion rate led to an anandamide concentration of approximately 1 mu M in venous blood as measured by mass spectrometry.4 Dermal application of anandamide significantly increased skin microcirculatory flow and coapplication of the transient receptor potential vanilloid 1 ( TRPV1) antagonist capsazepine inhibited this effect. The TRPV1 agonists capsaicin, olvanil and arvanil all induced concentration-dependent increases in skin blood flow and burning pain when administered dermally. Coapplication of capsazepine inhibited blood flow and pain responses to all three TRPV1 agonists.5 This study shows that locally applied anandamide is a vasodilator in the human skin microcirculation. The results are consistent with this lipid being an activator of TRPV1 on primary sensory nerves, but do not support a role for anandamide as a circulating vasoactive hormone in the human forearm vascular bed.

OBJECTIVES:To relate the pharmacokinetics of estradiol to pharmacological effects. METHODS:Drug concentration effect relationship of estradiol from two matrix transdermal delivery systems, Menorest and Climara, was studied in a single centre, open, randomised, comparative crossover study. The trial consisted of two treatment periods, 14 days for each patch separated by a 4-week washout period. Blood hormone levels were followed during the second week of each treatment. Estradiol levels during treatments were related to three concentration levels previously proposed as efficacy or safety limits. The effect of treatment on FSH-levels was examined and the relationship between the levels of estradiol and FSH was described using an inhibitory sigmoidal I(max) model. Estrone levels and estradiol/estrone before and during treatment were followed. RESULTS:The C(average) of FSH during treatment was 38% lower than baseline plasma levels. Estradiol had an inhibitory effect on FSH with an I(max) of 0.68 and an IC(50) of 19 pg/ml. The fraction of time above the minimum concentration for therapeutic effect and the tolerability limit did not differ between the two treatments, whereas the fraction of time above the suggested threshold for osteoporosis prophylaxis was significantly larger for Menorest than for Climara (P<0.05). The low baseline estradiol/estrone ratios increased towards pre-menopausal levels during treatment. CONCLUSIONS:The drug concentration effect relationship of estradiol may be of use in evaluation of the effects of prophylactic estrogen therapy and to facilitate comparisons between different forms of estrogen treatments.