Immune Network Modeling Predicts Specific Nasopharyngeal and Peripheral Immune Dysregulation in Otitis-Prone Children

A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies

We propose that one of the main hurdles in delivering comprehensively informed care results from the challenges surrounding the extraction, representation, and retention of prior clinical experience and basic medical knowledge, as well as its translation into time- and context-informed actionable interventions. While emerging applications in artificial intelligence-based techniques, for example, large language models, offer impressive pattern association capabilities, they often fall short in producing human-readable explanations crucial to their integration into clinical care. Moreover, they require large well-defined and well-integrated data sets that typically conflict with the availability of such data in all but a few areas of medicine, for example, medical imaging and neuroimaging, noninvasive monitoring of bio-electrical activity, etc. In this chapter, we argue that approximate reasoning rooted in the knowledge that is explainable to the human clinician may offer attractive avenues for the introduction of such knowledge in a systematic way that supports formal retention, sharing, and reuse of new clinical and basic medical experience. We outline a conceptual protocol that targets the use of sparse and disparate data of different types and from different sources, seamlessly drawing on our collective experience and that of others. We illustrate the utility of such an integrative approach by applying the latter to the assessment and reconciliation of data from different experimental models, human and animal, in the example use case of a complex health condition.

PANEL 3: Otitis media animal models, cell culture, tissue regeneration & pathophysiology

OBJECTIVE

To review and summarize recently published key articles on the topics of animal models, cell culture studies, tissue biomedical engineering and regeneration, and new models in relation to otitis media (OM).

DATA SOURCE

Electronic databases: PubMed, National Library of Medicine, Ovid Medline.

REVIEW METHODS

Key topics were assigned to the panel participants for identification and detailed evaluation. The PubMed reviews were focused on the period from June 2019 to June 2023, in any of the objective subject(s) or keywords listed above, noting the relevant references relating to these advances with a global overview and noting areas of recommendation(s). The final manuscript was prepared with input from all panel members.

CONCLUSIONS

In conclusion, ex vivo and in vivo OM research models have seen great advancements in the past 4 years. From the usage of novel genetic and molecular tools to the refinement of in vivo inducible and spontaneous mouse models, to the introduction of a wide array of reliable middle ear epithelium (MEE) cell culture systems, the next five years are likely to experience exponential growth in OM pathophysiology discoveries. Moreover, advances in these systems will predictably facilitate rapid means for novel molecular therapeutic studies.

Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.