Application of a Dynamic Map for Learning, Communicating, Navigating, and Improving Therapeutic Development

Cross-Species Extrapolation of Biological Data to Guide the Environmental Safety Assessment of Pharmaceuticals-The State of the Art and Future Priorities

The extrapolation of biological data across species is a key aspect of biomedical research and drug development. In this context, comparative biology considerations are applied with the goal of understanding human disease and guiding the development of effective and safe medicines. However, the widespread occurrence of pharmaceuticals in the environment and the need to assess the risk posed to wildlife have prompted a renewed interest in the extrapolation of pharmacological and toxicological data across the entire tree of life. To address this challenge, a biological "read-across" approach, based on the use of mammalian data to inform toxicity predictions in wildlife species, has been proposed as an effective way to streamline the environmental safety assessment of pharmaceuticals. Yet, how effective has this approach been, and are we any closer to being able to accurately predict environmental risk based on known human risk? We discuss the main theoretical and experimental advancements achieved in the last 10 years of research in this field. We propose that a better understanding of the functional conservation of drug targets across species and of the quantitative relationship between target modulation and adverse effects should be considered as future research priorities. This pharmacodynamic focus should be complemented with the application of higher-throughput experimental and computational approaches to accelerate the prediction of internal exposure dynamics. The translation of comparative (eco)toxicology research into real-world applications, however, relies on the (limited) availability of experts with the skill set needed to navigate the complexity of the problem; hence, we also call for synergistic multistakeholder efforts to support and strengthen comparative toxicology research and education at a global level. Environ Toxicol Chem 2024;43:513-525. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Negative findings but positive contributions in cardiovascular research

Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We also believe in the importance and indispensability of negative results. Therefore, in this review, we discussed the factors contributing to the publication bias of negative results and the problems to assess the factuality and validity of negative results. Moreover, we emphasized the importance of reporting negative results in cardiovascular research, including treatments, and suggest that the negative results could clarify previously controversial topics in the treatment of cardiovascular diseases and prompt the translation of research on precision cardiovascular disease prevention and treatment.

Vaccine Innovation Meta-Model for Pandemic Contexts

Purpose

Over the past decade, successive outbreaks and epidemics of infectious diseases have challenged the emergency preparedness and response systems of global public health institutions, a context in which vaccines have become the centerpiece to strengthening global health security. Nevertheless, vaccine research and development (R&D) is a complex, lengthy, risky, uncertain, and expensive process. Alongside strict, time-consuming regulatory compliance, it takes multiple candidates and many years to register a new vaccine. This is certainly not welcome in a global health crisis such as the COVID-19 pandemic. Therefore, this study aims to understand the R&D paradigm shift in pandemic contexts and its impacts on the value chain of vaccine innovation.

Methods

To that end, this paper carried out a systematic literature review and meta-synthesis of 27 articles and reports (2011-2021) that addressed vaccine R&D in contexts of global health threats, disease outbreaks, epidemics, or pandemics.

Results

The research findings are synthesized in a meta-model, which describes a fast-track R&D for pandemic contexts, its driving forces, innovations, mechanisms, and impacts in the value chain of vaccine innovation.

Conclusions

The study demonstrates that, in pandemic contexts, a fast-track R&D process based on close collaboration among regulators, industry, and academia and leveraging enabling technologies can drastically reduce the time required to bring safe, stable, and effective vaccines to market by an average of 11 years compared to the traditional R&D process. Furthermore, pharmacovigilance and rigorous monitoring of real-world evidence became critical to ensuring that quality and safe products were authorized for use during a pandemic.

How resilient were we in 2021? Results of a LinkedIn Survey including biomedical and pharmaceutical professionals using the Benatti Resiliency Model

Enhancing resiliency should elevate innovation and efficiency in biomedical research and development (R&D); however, compared with other professions, data on practice of resilience is lacking. Using the Benatti Resiliency Model (5 anchors: Well-Being, Self-Awareness, Brand, Connection, and Innovation), we surveyed professionals, including those in biomedical and pharmaceutical R&D. A structured LinkedIn questionnaire (March 16-May 23, 2021), surveyed each model anchor using five categories. One hundred fifty-eight participants (~6% student/trainee, 18%, 27%, and 49% in 1-5, 5-15 or >15 years post-terminal degree) took the survey (90 in biomedical and pharmaceutical R&D). Over 50% chose "always"/"often" across questions, except external influence or engagement. The question with one of the lowest "always" scores (~15%) was "I get feedback on my influence and impact in my career" in Brand, highlighting areas for leadership development and coaching. In the anchor of Well-being, nutrition and stress management also received some lowest "always" scores (~15% for both). Connection and Innovation scores trended slightly higher in biomedical and pharmaceutical R&D. No students/trainees chose "always" in Brand, indicating evolution of brand maturity over time. Self- and survey-assessed resiliency scores were associated (r_s  = 0.37, p < 0.0001). Our survey yielded actionable insights on Resilience, including "best practices" through an open-ended question for one thing most useful to boost resilience in the survey and is the first application of the Benatti Model for crowdsourced research.

Microphysiological Systems: Stakeholder Challenges to Adoption in Drug Development

Microphysiological systems (MPS) or tissue chips/organs-on-chips are novel in vitro models that emulate human physiology at the most basic functional level. In this review, we discuss various hurdles to widespread adoption of MPS technology focusing on issues from multiple stakeholder sectors, e.g., academic MPS developers, commercial suppliers of platforms, the pharmaceutical and biotechnology industries, and regulatory organizations. Broad adoption of MPS technology has thus far been limited by a gap in translation between platform developers, end-users, regulatory agencies, and the pharmaceutical industry. In this brief review, we offer a perspective on the existing barriers and how end-users may help surmount these obstacles to achieve broader adoption of MPS technology.

Improving Translational Paradigms in Drug Discovery and Development

Despite improved knowledge regarding disease causality, new drug targets, and enabling technologies, the attrition rate for compounds entering clinical trials has remained consistently high for several decades, with an average 90% failure rate. These failures are manifested in an inability to reproduce efficacy findings from animal models in humans and/or the occurrence of unexpected safety issues, and reflect failures in T1 translation. Similarly, an inability to sequentially demonstrate compound efficacy and safety in Phase IIa, IIb, and III clinical trials represents failures in T2 translation. Accordingly, T1 and T2 translation are colloquially termed 'valleys of death'. Since T2 translation dealt almost exclusively with clinical trials, T3 and T4 translational steps were added, with the former focused on facilitating interactions between laboratory- and population-based research and the latter on 'real world' health outcomes. Factors that potentially lead to T1/T2 compound attrition include: the absence of biomarkers to allow compound effects to be consistently tracked through development; a lack of integration/'de-siloing' of the diverse discipline-based and technical skill sets involved in drug discovery; the industrialization of drug discovery, which via volume-based goals often results in quantity being prioritized over quality; inadequate project governance and strategic oversight; and flawed decision making based on unreliable/irreproducible or incomplete data. A variety of initiatives have addressed this problem, including the NIH National Center for Advancing Translational Sciences (NCATS), which has focused on bringing an unbiased academic perspective to translation, to potentially revitalize the process. This commentary provides an overview of the basic concepts involved in translation, along with suggested changes in the conduct of biomedical research to avoid valleys of death, including the use of Translational Scoring as a tool to avoid translational attrition and the impact of the FDA Accelerated Approval Pathway in lowering the hurdle for drug approval. © 2021 Wiley Periodicals LLC.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Microphysiological systems: What it takes for community adoption

Microphysiological systems (MPS) are promising in vitro tools which could substantially improve the drug development process, particularly for underserved patient populations such as those with rare diseases, neural disorders, and diseases impacting pediatric populations. Currently, one of the major goals of the National Institutes of Health MPS program, led by the National Center for Advancing Translational Sciences (NCATS), is to demonstrate the utility of this emerging technology and help support the path to community adoption. However, community adoption of MPS technology has been hindered by a variety of factors including biological and technological challenges in device creation, issues with validation and standardization of MPS technology, and potential complications related to commercialization. In this brief Minireview, we offer an NCATS perspective on what current barriers exist to MPS adoption and provide an outlook on the future path to adoption of these in vitro tools.

Quantitative retrospective natural history modeling for orphan drug development

The natural history of most rare diseases is incompletely understood and usually relies on studies with low level of evidence. Consistent with the goals for future research of rare disease research set by the International Rare Diseases Research Consortium in 2017, the purpose of this paper is to review the recently developed method of quantitative retrospective natural history modeling (QUARNAM) and to illustrate its usefulness through didactically selected analyses examples in an overall population of 849 patients worldwide with seven (ultra-) rare neurogenetic disorders. A quantitative understanding of the natural history of the disease is fundamental for the development of specific interventions and counseling afflicted families. QUARNAM has a similar relationship to a published case study as a meta-analysis has to an individual published study. QUARNAM relies on sophisticated statistical analyses of published case reports focusing on four research questions: How long does it take to make the diagnosis? How long do patients live? Which factors predict disease severity (eg, genotypes, signs/symptoms, biomarkers)? Where can patients be recruited for studies? Useful statistical techniques include Kaplan-Meier estimates, cluster analysis, regression techniques, binary decisions trees, word clouds, and geographic mapping. In comparison to other natural history study methods (prospective studies or retrospective studies such as chart reviews), QUARNAM can provide fast information on hard clinical endpoints (ie, survival, diagnostic delay) with a lower effort. The choice of method for a particular drug development program may be driven by the research question and may encompass combinatory approaches.

Organs-on-chips: into the next decade

Organs-on-chips (OoCs), also known as microphysiological systems or 'tissue chips' (the terms are synonymous), have attracted substantial interest in recent years owing to their potential to be informative at multiple stages of the drug discovery and development process. These innovative devices could provide insights into normal human organ function and disease pathophysiology, as well as more accurately predict the safety and efficacy of investigational drugs in humans. Therefore, they are likely to become useful additions to traditional preclinical cell culture methods and in vivo animal studies in the near term, and in some cases replacements for them in the longer term. In the past decade, the OoC field has seen dramatic advances in the sophistication of biology and engineering, in the demonstration of physiological relevance and in the range of applications. These advances have also revealed new challenges and opportunities, and expertise from multiple biomedical and engineering fields will be needed to fully realize the promise of OoCs for fundamental and translational applications. This Review provides a snapshot of this fast-evolving technology, discusses current applications and caveats for their implementation, and offers suggestions for directions in the next decade.

Organs-on-a-Chip

Organs-on-chips, also known as "tissue chips" or microphysiological systems (MPS), are bioengineered microsystems capable of recreating aspects of human organ physiology and function and are in vitro tools with multiple applications in drug discovery and development. The ability to recapitulate human and animal tissues in physiologically relevant three-dimensional, multi-cellular environments allows applications in the drug development field, including; (1) use in assessing the safety and toxicity testing of potential therapeutics during early-stage preclinical drug development; (2) confirmation of drug/therapeutic efficacy in vitro; and (3) disease modeling of human tissues to recapitulate pathophysiology within specific subpopulations and even individuals, thereby advancing precision medicine efforts. This chapter will discuss the development and evolution of three-dimensional organ models over the past decade, and some of the opportunities offered by MPS technology that are not available through current standard two-dimensional cell cultures, or three-dimensional organoid systems. This chapter will outline future avenues of research in the MPS field, how cutting-edge biotechnology advances are expanding the applications for these systems, and discuss the current and future potential and challenges remaining for the field to address.

Exploiting machine learning for end-to-end drug discovery and development

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

Towards improved process efficiency in vaccine innovation: The Vaccine Innovation Cycle as a validated, conceptual stage-gate model

Continuing investments in vaccine innovation are insufficiently translated into market entries of novel vaccines. This innovation paradox is in part caused by stakeholders lacking complete understanding of the complex array of steps necessary for vaccine development and collaboration difficulties between the wide variety of stakeholders involved. Models providing cross-domain understanding can improve collaboration but currently lack both comprehensibility and granularity to enable a prioritized view of activities and criteria. Key opinion leaders (KOLs) were asked to contribute to the definition of a vaccine innovation cycle (VIC). In a first step, 18 KOLs were interviewed on the stages (activities and results) and gates (evaluation criteria and outcomes) of vaccine innovation. This first description of the VIC was subsequently validated and refined through a survey among 46 additional KOLs. The VIC identifies 29 distinct stages and 28 corresponding gates, distributed in ten different but integrated workstreams, and comprehensibly depicted in a circular innovation model. Some stage-gates occur at defined moments, whereas the occurrence and timing of other stage-gates is contingent on a variety of contextual factors. Yet other stage-gates continuously monitor internal and external developments. A gap-overlap analysis of stage-gate criteria demonstrated that 5 out of 11 criteria employed by vaccine developers correspond with criteria employed by competent (regulatory) authorities. The VIC provides a comprehensive overview of stage-gates throughout the value chain of vaccine innovation. Its cyclical nature highlights the importance of synchronizing with unmet needs and market changes, and conceptualizes the difference between incremental and radical vaccine innovation. Knowledge on the gap between internal and external criteria will enhance the viability of newcomers to the field. The VIC can be used by stakeholders to improve understanding and communication in forming collaborative alliances and consortia. Such a boundary-spanning function may contribute to the reduction of process inefficiencies, especially in public-private partnerships.

A cross-sectional quantitative analysis of the natural history of free sialic acid storage disease-an ultra-orphan multisystemic lysosomal storage disorder

PURPOSE

Quantitative definition of the natural history of free sialic acid storage disease (SASD, OMIM 604369), an orphan disorder due to the deficiency of the proton-driven carrier SLC17A5.

METHODS

Analysis of published cases with SASD (N = 116) respecting STROBE criteria.

MAIN OUTCOME PARAMETERS

survival and diagnostic delay. Phenotype, phenotype-biomarker associations, and geographical patient distribution were explored.

RESULTS

Median age at disease onset was 0.17 years. Median age at diagnosis was 3 years with a median diagnostic delay of 2.5 years. Median survival was 11 years. The biochemical phenotype clearly predicted the disease course: patients with a urinary free sialic acid excretion below 6.37-fold or an intracellular free sialic acid storage in fibroblasts below 7.37-fold of the mean of normal survived longer than patients with biochemical values above these thresholds. Cluster analysis of disease features suggested a continuous phenotypic spectrum. Patient distribution was panethnic.

CONCLUSION

Combination of neurologic symptoms, visceromegaly, and dysmorphic features and/or nonimmune hydrops fetalis should prompt specific tests for SASD, reducing diagnostic delay. The present quantitative data inform clinical studies and may stimulate and accelerate development of specific therapies. Biomarker-phenotype association is particularly important for both counseling parents and study design.