Preventive Pharmacovigilance: timely and precise prevention of adverse events through person-level patient screening and dose-level product surveillance.

The goal of pharmacovigilance (PV) is to prevent adverse events (AEs) associated with drugs and vaccines. Current PV programs are of a reactive nature and rest entirely on data science, i.e., detecting and analyzing AE data from provider/patient reports, health records and even social media. The ensuing preventive actions are too late for people who have experienced AEs and often overly broad, as responses include entire product withdrawals, batch recalls, or contraindications of subpopulations. To prevent AEs in a timely and precise manner, it is necessary to go beyond data science and incorporate measurement science into PV efforts through person-level patient screening and dose-level product surveillance. Measurement-based PV may be called 'preventive pharmacovigilance', the goal of which is to identify susceptible individuals and defective doses to prevent AEs. A comprehensive PV program should contain both reactive and preventive components by integrating data science and measurement science.

Sedimentation behavior of quality and freeze-damaged aluminum-adjuvanted vaccines by wNMR.

Vaccine sedimentation and resuspension are properties that vaccine makers use to characterize a suspension product during research and development as well as throughout the shelf life of the vaccine. Three vaccines with three different aluminum adjuvants and different antigens were selected and monitored over the course of sedimentation using water proton nuclear magnetic resonance (wNMR) relaxometry. This simple method measured fully intact, single-dose vaccine vials and reported sedimentation profiles for each, which readily distinguished freeze-stressed vaccines from unstressed vaccines.

Making in silico predictive models for toxicology FAIR.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

Assessing Antigen-Adjuvant Complex Stability Against Physical Stresses By wNMR.

This study applies an emerging analytical technology, wNMR (water proton nuclear magnetic resonance), to assess the stability of aluminum adjuvants and antigen-adjuvant complexes against physical stresses, including gravitation, flow and freeze/thaw. Results from wNMR are verified by conventional analytical technologies, including static light scattering and microfluidic imaging. The results show that wNMR can quickly and noninvasively determine whether an aluminum adjuvant or antigen-adjuvant complex sample has been altered by physical stresses.

Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database.

Virtual Control Groups (VCGs) based on Historical Control Data (HCD) in preclinical toxicity testing have the potential to reduce animal usage. As a case study we retrospectively analyzed the impact of replacing Concurrent Control Groups (CCGs) with VCGs on the treatment-relatedness of 28 selected histopathological findings reported in either rat or dog in the eTOX database. We developed a novel methodology whereby statistical predictions of treatment-relatedness using either CCGs or VCGs of varying covariate similarity to CCGs were compared to designations from original toxicologist reports; and changes in agreement were used to quantify changes in study outcomes. Generally, the best agreement was achieved when CCGs were replaced with VCGs with the highest level of similarity; the same species, strain, sex, administration route, and vehicle. For example, balanced accuracies for rat findings were 0.704 (predictions based on CCGs) vs. 0.702 (predictions based on VCGs). Moreover, we identified covariates which resulted in poorer identification of treatment-relatedness. This was related to an increasing incidence rate divergence in HCD relative to CCGs. Future databases which collect data at the individual animal level including study details such as animal age and testing facility are required to build adequate VCGs to accurately identify treatment-related effects.

Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database.

Preclinical inter-species concordance can increase the predictivity of observations to the clinic, potentially reducing drug attrition caused by unforeseen adverse events. We quantified inter-species concordance of histopathological findings and target organ toxicities across four preclinical species in the eTOX database using likelihood ratios (LRs). This was done whilst only comparing findings between studies with similar compound exposure (Δ|Cmax| ≤ 1 log-unit), repeat-dosing duration, and animals of the same sex. We discovered 24 previously unreported significant inter-species associations between histopathological findings encoded by the HPATH ontology. More associations with strong positive concordance (33% LR+ > 10) relative to strong negative concordance (12.5% LR- < 0.1) were identified. Of the top 10 most positively concordant associations, 60% were computed between different histopathological findings indicating potential differences in inter-species pathogenesis. We also observed low inter-species target organ toxicity concordance. For example, liver toxicity concordance in short-term studies between female rats and dogs observed an average LR+ of 1.84, and an average LR- of 0.73. This was corroborated by similarly low concordance between rodents and non-rodents for 75 candidate drugs in AstraZeneca. This work provides new statistically significant associations between preclinical species, but finds that concordance is rare, particularly between the absence of findings.

eTRANSAFE: Building a sustainable framework to share reproducible drug safety knowledge with the public domain.

Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combination to aid meaningful interpretation and analysis of such data. The eTRANSAFE Consortium's effort to drive integrative drug safety research at a large scale hereby present the lessons learnt and the proposal of solution at the guidelines in practice at this Innovative Medicines Initiative (IMI) project. Recommendations in these guidelines were compiled from feedback received from key stakeholders in regulatory agencies, EFPIA companies, and academic partners. The research reproducibility guidelines presented in this study lay the foundation for a comprehensive data sharing and knowledge management plans accounting for research data management in the drug safety space - FAIR data sharing guidelines, and the model verification guidelines as generic deliverables that best practices that can be reused by other scientific community members at large. FAIR data sharing is a dynamic landscape that rapidly evolves with fast-paced technology advancements. The research reproducibility in drug safety guidelines introduced in this study provides a reusable framework that can be adopted by other research communities that aim to integrate public and private data in biomedical research space.

Inspecting Insulin Products Using Water Proton NMR. I. Noninvasive vs Invasive Inspection.

BACKGROUND: There is a clear need to transition from batch-level to vial/syringe/pen-level quality control of biologic drugs, such as insulin. This could be achieved only by noninvasive and quantitative inspection technologies that maintain the integrity of the drug product. METHODS: Four insulin products for patient self-injection presented as prefilled pens have been noninvasively and quantitatively inspected using the water proton NMR technology. The inspection output is the water proton relaxation rate R2(1H2O), a continuous numerical variable rather than binary pass/fail. RESULTS: Ten pens of each product were inspected. R2(1H2O) displays insignificant variation among the 10 pens of each product, suggesting good insulin content uniformity in the inspected pens. It is also shown that transferring the insulin solution out of and then back into the insulin pen caused significant change in R2(1H2O), presumably due to exposure to O2 in air. CONCLUSIONS: Water proton NMR can noninvasively and quantitatively inspect insulin pens. wNMR can confirm product content uniformity, but not absolute content. Its sensitivity to sample transferring provides a way to detect drug product tampering. This opens the possibility of inspecting every pen/vial/syringe by manufacturers and end-users.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives.

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Guidelines for FAIR sharing of preclinical safety and off-target pharmacology data.

Pre-competitive data sharing can offer the pharmaceutical industry significant benefits in terms of reducing the time and costs involved in getting a new drug to market through more informed testing strategies and knowledge gained by pooling data. If sufficient data is shared and can be co-analyzed, then it can also offer the potential for reduced animal usage and improvements in the in silico prediction of toxicological effects. Data sharing benefits can be further enhanced by applying the FAIR Guiding Principles, reducing time spent curating, transforming and aggregating datasets and allowing more time for data mining and analysis. We hope to facilitate data sharing by other organizations and initiatives by describing lessons learned as part of the Enhancing TRANslational SAFEty Assessment through Integrative Knowledge Management (eTRANSAFE) project, an Innovative Medicines Initiative (IMI) partnership which aims to integrate publicly available data sources with proprietary preclinical and clinical data donated by pharmaceutical organizations. Methods to foster trust and overcome non-technical barriers to data sharing such as legal and IPR (intellectual property rights) are described, including the security requirements that pharmaceutical organizations generally expect to be met. We share the consensus achieved among pharmaceutical partners on decision criteria to be included in internal clearance pro­cedures used to decide if data can be shared. We also report on the consensus achieved on specific data fields to be excluded from sharing for sensitive preclinical safety and pharmacology data that could otherwise not be shared.

Grand Challenges in Pharmaceutical Research Series: Ridding the Cold Chain for Biologics.

Biologics are complex pharmaceuticals that include formulated proteins, plasma products, vaccines, cell and gene therapy products, and biological tissues. These products are fragile and typically require cold chain for their delivery and storage. Delivering biologics, while maintaining the cold chain, whether standard (2°C to 8°C) or deepfreeze (as cold as -70°C), requires extensive infrastructure that is expensive to build and maintain. This poses a huge challenge to equitable healthcare delivery, especially during a global pandemic. Even when the infrastructure is in place, breaches of the cold chain are common. Such breaches may damage the product, making therapeutics and vaccines ineffective or even harmful. Rather than strengthening the cold chain through building more infrastructure and imposing more stringent guidelines, we suggest that money and effort are best spent on making the cold chain unnecessary for biologics delivery and storage. To meet this grand challenge in pharmaceutical research, we highlight areas where innovations are needed in the design, formulation and biomanufacturing of biologics, including point-of-care manufacturing and inspection. These technological innovations would rely on fundamental advances in our understanding of biomolecules and cells.

Quality assurance at the point-of-care: Noninvasively detecting vaccine freezing variability using water proton NMR.

Aluminum-adjuvanted vaccines are freeze-sensitive products that require attentive cold chain adherence. Freeze/thaw events can be tested using "The World Health Organization Shake Test", a qualitative test whereby a vial from the batch suspected to have been frozen is checked to infer whether the whole batch has been frozen. In this paper, we present a noninvasive and quantitative method to detect whether a vial of liquid vaccine has experienced freeze/thaw using the water proton transverse relaxation rate by Nuclear Magnetic Resonance relaxometry (wNMR relaxometry). Importantly, wNMR relaxometry does not compromise the vial's integrity so the analyzed vial can be used for vaccination if it meets the quality specifications. Vial-to-vial variability in freezing susceptibility within a single carton of vaccine vials was also detected, both by visual observation and concurrently by wNMR relaxometry. This variability brings into question the practice of using one or a few vials in a batch of vaccines to infer about the quality of the whole batch.

Magnetic Resonance Relaxometry for Determination of Protein Concentration and Aggregation.

The water-proton signal, overwhelmingly considered a nuisance in nuclear magnetic resonance spectroscopy, is advantageously used as a tool to assess protein concentration and to detect protein aggregates in aqueous solutions. The protocols in this article describe use of the water-proton transverse relaxation rate to determine concentration and aggregate content in protein solutions. Detailed recommendations and description of the parameter settings and data processing ensure successful implementation of this technique, even by a user with limited experience in magnetic resonance relaxometry. All measurements are done noninvasively, in a sealed container, without sampling or otherwise aliquoting the solution. The magnetic resonance relaxometry approach offered in this article could be advantageous for analysis of biologics formulations or when use of conventional analytical techniques is not possible. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Nuclear magnetic resonance (NMR) relaxometry to measure protein concentration Basic Protocol 2: NMR relaxometry to measure protein aggregation.

Flow Water Proton NMR: In-Line Process Analytical Technology for Continuous Biomanufacturing.

Continuous manufacturing of biologics is one of the priorities of the biopharmaceutical industry. However, its widespread implementation is hampered by a lack of noninvasive/nondestructive process analytical technology (PAT) systems capable of real-time in-line monitoring of product flow parameters, such as concentration and/or aggregate content. We have previously demonstrated that, under nonflow conditions, the water proton transverse relaxation rate, R2(1H2O), is sensitive to protein concentration and aggregate content in biopharmaceutical formulations. In the present work, we explored the potential of water proton NMR under flow conditions (flow-wNMR) to use R2(1H2O) as a quantitative indicator of protein concentration variations and aggregate levels in the process flow. We show that, under flow conditions, R2(1H2O) is sensitive to rather small changes in protein concentration (<1 mg/mL) and is capable to detect variations in the aggregate content of <1%. Our findings suggest that flow-wNMR could be advantageously used as a real-time in-line noninvasive PAT for continuous biomanufacturing.

Correction to: Nondestructive Quantitative Inspection of Drug Products Using Benchtop NMR Relaxometry-the Case of NovoMix® 30.

Typesetting error occurred and author corrections to the equations and text edits at the proofing stage were not incorporated in the published article. The original article has been corrected.