Emerging technologies and their impact on regulatory science

Abstract

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

Keywords: Emerging technologies; bioimaging; bioinformatics; biomarkers; regulatory science; risk assessment.

Figure 1.

Definition of machine learning and deep learning.

Figure 2.

Steps in product development.

Figure 3.

History of the Sentinel Initiative.

Figure 4.

Sentinel System Innovation Strategies.

Figure 5.

Four-pronged approach to Sentinel Innovation Strategy Implementation.

Figure 6.

Sample cloud tag for “severe adverse events,” the actual cloud tags contained more than 14,000 terms.

Figure 7.

Description of main steps of the performed systematic review with corresponding automation tools and obtained numbers of publications.

Figure 8.

Predictive, personalized, participatory, and precision health (i.e., pHealth) enabled by the advancement of emerging biomedical technologies. Multi-modal biomedical data of the same patient are combined by AI-models to provide a comprehensive evaluation of the patient.

Figure 9.

An exemplary pipeline for AI-enabled pHealth model using RNA-seq data. (a). Raw -omics data are collected with high-throughput sequencing techniques. (b) Multi-omics features are extracted with various bioinformatics pipelines. (c) AI-based predictive models are utilized.

Figure 10.

Precision was uniformly high across the ctDNA assays.

Figure 11.

The PCA indicates the variation in the experiments did not alter the outcome to any large extent. After PLS-discriminate analysis, we were able to cluster the samples based on how we processed them. The ones that showed the largest effect was the blood left at room temperature for six hours or the plasma which was left at room temperature for 24 h (adapted from reference 41).

Figure 12.

The palmitoyl carnitine peak appears before ALT peak as observed in rodents and humans (adapted from reference 47).

Figure 13.

Approximately 3800 peaks differentiated high opium users from non-opium users, and approximately 712 peaks differentiated high opium users diagnoses as OUD positive from high opium users diagnosed as OUD negative.

Figure 14.

An outline of the role and impact of genomics, proteomics, and metabolomics in assessing the safety and evaluating the provenance of foods, drugs, and cosmetics for regulatory science.

Figure 15.

A typical workflow for in silico metabolomics and the utility of BioTransformer in identifying chemical “dark matter.”

Figure 16.

Initiatives that have contributed to metabolomics scientific reproducibility and standardization.

Figure 17.

AMED-MPS Project in Japan. Researchers from the National Institute of Advanced Industrial Science and Technology (AIST) and the National Institute of Health Sciences (NIHS) coordinate the Device Manufacturing program, Standardization program of MPSs developed by universities and other academic institutions in Cell supply and Model development program. Researchers from pharmaceutical companies participate in Standardization program. Central Research Center provides a place for research and development.

Figure 18.

Hepatic Sinusoid and regional function. Hepatocytes within the lobule shows different functions in region dependent manner.

Figure 19.

Standard procedure of the acceptance of new test method as a test guideline in OECD.

Figure 20.

Overview of the European Roadmap for OoC Development.

Figure 21.

Value chain and roadblocks from an MPS invention towards a validated MPS-based assay benefiting patients.

Figure 22.

Constructive interdependent qualification processes for MPS-based context-of-use assays at TissUse. Equipment qualification includes Installation qualification (IQ), Operational qualification (OQ) and performance qualification (PQ) at end-user sites after technology transfer.

Figure 23.

Readiness levels defined at TissUse to easy customer communication, collaboration, and technology transfer management.

Figure 24.

MSC aggregates in chondrogenic condition. (a) representative images of two MSC lines (MSC-A and MSC-B) at early passage (passage 2 or 3) at day 7 (D7) and day 21 (D21); (b) MSCs at late passage (passage 5) at D7 and D21; (c, d) histology images that were analyzed to confirm the deposition of cartilage-associated extracellular matrices (GAG shown in blue and collagen shown in red). The histology data show that the cells lines recovered in size deposited higher amounts of cartilage-associated extracellular matrices compared to the cell lines did not show the recovery (d).

Figure 25.

Regulatory requirements and measures.

Figure 26.

BBB chip with iPSC-derived brain microvascular endothelial cells (BMECs) and neural cells.

Figure 27.

Motor neurons from iPSCs derived from patients with ALS. These have been seeded onto chips along with BMECs from the same iPSC line.

Figure 28.

A tiered approach to regulatory science – starting with 2D-based screening, but then proceeding with more biologically-relevant 3D human models.

Figure 29.

Tiered workflow for establishing the reproducibility and context of use for MPS at Texas A&M University (TAMU) Tissue Chip Testing Center.

Figure 30.

Representative example of the T2 fitting in the cerebrospinal fluid (●), gray matter (○), and white matter (▼). The image in the top right corner shows the calculated T2 map of the rat brain with the location of the fitted voxels (pointed to using the black lines). Each point in the graph represents the actual image intensity value (in institutional units, y-axis) in the given area of the echo image with the corresponding echo time (x-axis). A total of 16 echo images with 15 ms echo spacing were used. Black curves on the graph show the best fit function using the equation M 5 M0*exp(–TE/T2), where M is the image intensity, M0 is the image intensity of proton density image (at echo time = 0 ms), TE is echo time, and T2 is the sought parameter. The T2 map is color-coded according to the color scale on the right, each voxel in the map representing the observed quantitative T2 relaxation in milliseconds.

Figure 31.

Representative T2 maps for animals which showed signs of brain alterations in response to treatment with known neurotoxicants. Control animal treated with saline (2 ml/kg, once) 48 h before imaging; 3-nitropropionic acid was administered in a dose of 20 mg/kg, s.c., daily for three days, imaging on day 4; hexachlorophene was administered in a dose of 30 mg/kg, p.o. daily for five days, imaging on day 6; kainic acid was administered in a dose of 10 mg/kg, i.p., once, imaging on day 3; domoic acid was administered in a dose of 2 mg/kg, i.p., once, imaging on day 3; pyrithiamine was administered in a dose of 0.25 mg/kg i.p. daily for two weeks + thiamine free diet, imaging at four weeks; trimethyltin was administered in a dose of 12 mg/kg, i.p., once, imaging at three weeks.

Figure 32.

T2 maps of a representative animal obtained at various time points (days, shown above each map) after hexachlorophene treatment. A single slice out of a 24-slice pack is shown. The color scale (on the right) relates to the actual T2 relaxation time (in milliseconds) for each voxel. Note the maximum T2 values in the white matter occurred at day 6. T2 maps of the control subjects (not shown) were not different from T2 maps at time 0.

Figure 33.

Performance of iBiopsy® in advanced fibrosis prediction. CRN: Clinical Research Network; ROC: receiver operating characteristic.

Figure 34.

Impact of fibrosis burden on HCC recurrence.

Figure 35.

Modulation of exposure-response relationships: mechanisms.

Figure 36.

A type of study design used to assess microbiome influences on toxicity (many variations on this design have been used).

Figure 37.

A tiered approach for the risk assessment including the gut microbiome.