Unmet needs: Research helps regulators do their jobs

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

Transporters in Regulatory Science: Notable Contributions from Dr. Giacomini in the Past Two Decades

Transporters govern the access of molecules to cells or their exit from cells, thereby controlling the overall distribution of drugs to their intracellular site of action. Clinically relevant drug-drug interactions mediated by transporters are of increasing interest in drug development. Drug transporters, acting alone or in concert with drug metabolizing enzymes, can play an important role in modulating drug absorption, distribution, metabolism, and excretion, thus affecting the pharmacokinetics and/or pharmacodynamics of a drug. Dr. Kathy Giacomini from the University of California, San Francisco is one of the world leaders in transporters and pharmacogenetics with key contributions to transporter science. Her contributions to transporter science are noteworthy. This review paper will summarize Dr. Giacomini's key contributions and influence on transporters in regulatory science in the past two decades. Regulatory science research highlighted in this review covers various aspects of transporter science including understanding the effect of renal impairment on transporters, transporter ontogeny, biomarkers for transporters, and interactions of excipients with transporters affecting drug absorption. Significance Statement This review paper highlights Dr. Giacomini's key contributions and influence on transporters in regulatory science in the past two decades. She has been at the cutting edge of science pertaining to drug transport, drug disposition, and regulatory science, leading to new era of translational sciences pertaining to drug disposition and transporter biology. Her research has and will continue to bring enormous impact on gaining new knowledge in guiding drug development and inspire scientists from all sectors in the field.

One in seven pathogenic variants can be challenging to detect by NGS: an analysis of 450,000 patients with implications for clinical sensitivity and genetic test implementation

PURPOSE

To evaluate the impact of technically challenging variants on the implementation, validation, and diagnostic yield of commonly used clinical genetic tests. Such variants include large indels, small copy-number variants (CNVs), complex alterations, and variants in low-complexity or segmentally duplicated regions.

METHODS

An interlaboratory pilot study used synthetic specimens to assess detection of challenging variant types by various next-generation sequencing (NGS)-based workflows. One well-performing workflow was further validated and used in clinician-ordered testing of more than 450,000 patients.

RESULTS

In the interlaboratory study, only 2 of 13 challenging variants were detected by all 10 workflows, and just 3 workflows detected all 13. Limitations were also observed among 11 less-challenging indels. In clinical testing, 21.6% of patients carried one or more pathogenic variants, of which 13.8% (17,561) were classified as technically challenging. These variants were of diverse types, affecting 556 of 1,217 genes across hereditary cancer, cardiovascular, neurological, pediatric, reproductive carrier screening, and other indicated tests.

CONCLUSION

The analytic and clinical sensitivity of NGS workflows can vary considerably, particularly for prevalent, technically challenging variants. This can have important implications for the design and validation of tests (by laboratories) and the selection of tests (by clinicians) for a wide range of clinical indications.

Regulatory Science and Innovation Programme for Europe (ReScIPE): A proposed model

Regulatory science underpins the objective evaluation of medicinal products. It is therefore imperative that regulatory science and expertise remain at the cutting edge so that innovations of ever-increasing complexity are translated safely and swiftly into effective, high-quality therapies. We undertook a comprehensive examination of the evolution of science and technology impacting on medicinal product evaluation over the next 5-10 years and this horizon-scanning activity was complemented by extensive stakeholder interviews, resulting in a number of significant recommendations. Highlighted in particular was the need for expertise and regulatory science research to fill knowledge gaps in both more fundamental, longer-term research, with respect to technological and product-specific challenges. A model is proposed to realise these objectives in Europe, comprising a synergistic relationship between the European Medicines Agency, the European Medicines Regulatory Network and academic research centres to establish a novel regulatory science and innovation platform.

Regulatory Science to 2025: An Analysis of Stakeholder Responses to the European Medicines Agency's Strategy

The pace of innovation is accelerating, and so medicines regulators need to actively innovate regulatory science to protect human and animal health. This requires consideration and consultation across all stakeholder groups. To this end, the European Medicines Agency worked with stakeholders to draft its Regulatory Science Strategy to 2025 and launched it for public consultation. The responses to this consultation were analyzed qualitatively, using framework analysis and quantitatively, to derive stakeholders' aggregate scores for the proposed recommendations. This paper provides a comprehensive resource of stakeholder positions on key regulatory science topics of the coming 5 years. These stakeholder positions have implications for the development and regulatory approval of both human and veterinary medicines.

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

Toward multimodal signal detection of adverse drug reactions

OBJECTIVE

Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation.

MATERIAL AND METHODS

Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates.

RESULTS

Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark.

CONCLUSIONS

The results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals.

Towards precision medicine

There is great potential for genome sequencing to enhance patient care through improved diagnostic sensitivity and more precise therapeutic targeting. To maximize this potential, genomics strategies that have been developed for genetic discovery - including DNA-sequencing technologies and analysis algorithms - need to be adapted to fit clinical needs. This will require the optimization of alignment algorithms, attention to quality-coverage metrics, tailored solutions for paralogous or low-complexity areas of the genome, and the adoption of consensus standards for variant calling and interpretation. Global sharing of this more accurate genotypic and phenotypic data will accelerate the determination of causality for novel genes or variants. Thus, a deeper understanding of disease will be realized that will allow its targeting with much greater therapeutic precision.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.