Big Data - How to Realize the Promise

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

PURPOSE

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

METHODS

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

FINDINGS

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

IMPLICATIONS

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

A Data Ingestion Procedure towards a Medical Images Repository

This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients' sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images.

OBJECTIVES

This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP.

METHODOLOGY

Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery.

OUTCOMES

We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.

A framework for chemical safety assessment incorporating new approach methodologies within REACH

The long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution.

The impact of a multilevel interactive nursing quality control and audit application on nursing quality management

Background

This study aims to investigate the effects of a nursing quality control and audit application (app) on the autonomous learning of nursing staff and nursing quality management by nursing supervisors. A multilevel interactive app is developed to assist nursing staff in conducting online autonomous learning and nursing supervisors in identifying problems and creating nursing quality improvement plans. The app could also present the different evaluation results of wards in visual charts for supervisors to review.

Methods

A single-group pre- and post-test design was applied. Data were collected from 131 nurses between October 2019 and October 2020 to analyze the differences between nursing staffs’ willingness to engage in autonomous learning and the integrity of nursing quality improvement plan writing before and after the intervention. The structured questionnaires included open-ended questions that cover aspects of nursing quality control, the audit app, and the information acceptance intention of nurses.

Results

The participants’ age and job title are negatively correlated with the app’s usability, while the ability to use 3C (Computer, Communication, and Consumer Electronics products including mobile phones and laptops) equipment is positively correlated with the willingness to use the app. Nurses’ satisfaction with the convenience of the online autonomous learning method is 92%, which indicates that the app could improve their willingness to learn. Following the intervention of the app, nursing supervisors’ satisfaction with the integrity of nursing quality improvement plan writing increased from 41 to 88%.

Conclusions

Using information technology products to assist in nursing quality management in clinical practice has a significant effect on nurses’ load reduction and head nurses’ satisfaction. Multilevel interactive nursing quality control and audit apps can improve nursing staff’s willingness to learn independently, nursing quality, and the integrity of plan writing. Thus, nursing quality control and audit apps can be considered as suitable nursing quality control tools.

Dynamic Regulatory Assessment: evolving the European Regulatory Framework for the Benefit of Patients and Public Health-an EFPIA View

The European Union regulatory framework supports development, review, authorization, and maintenance of medicines to benefit public health; however, many elements are 2 decades old and undergoing review. Scrutiny was triggered by the coronavirus disease 2019 pandemic, the need to support future innovative medicines, the digital transformation of data exchange, and the need to address efficiency and capacity limitations. There are also ongoing evolutions in regulatory science for medicines (eg, cell and gene therapies), medical device combinations, and software, as well as the need to fully leverage contemporary information technology (IT). Important initiatives to address these challenges include the European Medicines Agency (EMA) Regulatory Science Strategy,¹ the EU Regulatory Network Strategy,² and the Big Data Steering Group,³ alongside European Commission-led initiatives such as the Pharmaceutical Strategy.⁴ Dynamic regulatory assessment (DRA) is a concept that seeks to integrate these various elements to re-imagine regulatory review interactions across the product life cycle. DRA calls for iterative regulatory dialogue, data submission, and evidence assessment, enabled by contemporary IT. DRA will facilitate iterative interaction and data assessment as it accumulates over a product's life cycle, bringing significant efficiencies for all product types. The DRA concept primarily evolved through dialogue within working groups of the European Federation of Pharmaceutical Industries and Associations. This article describes the long-term vision of the European Federation of Pharmaceutical Industries and Associations and outlines important strategic elements of progress, including: aligning on a multi-stakeholder vision for DRA in the European Union and across regions; leveraging learnings from ongoing initiatives; and advancing IT, governance, and standards considerations. Ultimately, DRA should consider outcomes that deliver optimal benefits for patients in the European Union and worldwide.

Event detection in temporal social networks using a higher-order network model

Event detection is one of the most important areas of complex network research. It aims to identify abnormal points in time corresponding to social events. Traditional methods of event detection, based on first-order network models, are poor at describing the multivariate sequential interactions of components in complex systems and at accurately identifying anomalies in temporal social networks. In this article, we propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks. Given binary sequential data, we take advantage of chronological order to recover the multivariate sequential data first. Meanwhile, we develop new multivariate sequential data using logical sequence. Through the efficient modeling of multivariate sequential data using a higher-order network model, some common multivariate interaction patterns are obtained, which are used to determine the anomaly degree of a social event. Experiments in temporal social networks demonstrate the significant performance of our methods finally. We believe that our methods could provide a new perspective on the interplay between event detection and the application of higher-order network models to temporal networks.

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model

Real world evidence (RWE) and real-world data (RWD) are drawing ever-increasing attention in the pharmaceutical industry and drug regulatory authorities (DRAs) all over the world due to their paramount role in supporting drug development and regulatory decision making. However, there is little systematic documentary analysis about how RWE was integrated for the use by the DRAs in evaluating new treatment approaches and monitoring post-market safety. This study aimed to analyze and discuss the integration of RWE into regulatory decision-making process from the perspective of DRAs. Different development strategies to develop and adopt RWE by the DRAs in the US, Europe, and China were reviewed and compared, and the challenges encountered were discussed. It was found that different strategies on development of RWE were applied by FDA, EMA, and NMPA. The extent to which RWE was adopted in China was relatively limited compared to that in the US and EU, which was highly related to the national pharmaceutical environment and development stages. A better understanding of the overall goals, inputs, activities, outputs, and outcomes in developing RWE will help inform actions to harness RWD and leverage RWE for better health care decisions.

From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.

Using RWE research to extend clinical trials in diabetes: An example with implications for the future

BACKGROUND

Although randomized, controlled trials (RCTs) are seen as the gold standard for evidence in clinical medicine, a number of considerations are increasing the use of real-world data (RWD) to generate evidence. A series of methodological challenges must be overcome in order for such real-world evidence (RWE) to gain acceptance. In diabetes, RWE faces some particular issues that have limited its development. As the natural history of diabetes progresses, patients' disease changes over time and treatments will be modified as a result. This evolving disease and treatment pattern requires application of methods that account for such changes over time. Research developing RWE in diabetes and other conditions has sometimes been subject to important biases, and researchers should be aware of, and take steps to mitigate potential for bias in order to enhance the evidence produced.

RESULTS

We review a RWE study that replicated and extended evidence provided by a RCT regarding the effects of weekly exenatide relative to basal insulin (glargine or detemir) to illustrate a potential application of RWE. This study observed a 0.7% decrease in HbA1C for weekly exenatide relative to a 0.5% decrease in HbA1C for the comparator along with a 2 kg weight loss for weekly exenatide relative to a 0.25 kg weight gain, effects that were close to those from the RCT. Further, the RWE study was able to extend results to patient populations that were not well represented in the RCT.

CONCLUSION

Despite numerous challenges, RWE can be used to complement evidence from RCTs.