Embracing the future of medicine with virtual patients.

The adoption of virtual patients promises to revolutionize pharmaceutical R&D and health care through more patient-centric approaches and is driven by integrating innovations in computational approaches, AI, and advanced in vitro human model systems. Virtual patients can create the conditions for faster and more predictive R&D, affecting all drivers of R&D productivity. Virtual patients enable personalized health care, ensuring precise drug dosing and scheduling to maximize efficacy and minimize adverse effects.

Self-supervised hybrid neural network to achieve quantitative bioluminescence tomography for cancer research.

Bioluminescence tomography (BLT) improves upon commonly-used 2D bioluminescence imaging by reconstructing 3D distributions of bioluminescence activity within biological tissue, allowing tumor localization and volume estimation-critical for cancer therapy development. Conventional model-based BLT is computationally challenging due to the ill-posed nature of the problem and data noise. We introduce a self-supervised hybrid neural network (SHyNN) that integrates the strengths of both conventional model-based methods and machine learning (ML) techniques to address these challenges.

Compact photonic model based on coupled-mode theory for nonlinear interactions in electronic-photonic co-simulation.

This paper demonstrates a Verilog-A compact photonic model based on coupled-mode theory for nonlinear interactions, including four-wave mixing (FWM) and cross-phase modulation (XPM), to present a general framework and methodology for modeling nonlinear interactions in electronic-photonic co-simulation. The model is compatible with existing electronic design automation (EDA) platforms and can support rapid electronic-photonic co-simulation. It avoids describing the complicated physical process of the FWM and provides an easy way for system designers to monitor the dynamics of the critical optical parameters, thus accelerating the co-design and co-optimization of the electronic-photonic hybrid systems incorporating FWM.

Keplerate polyoxometalate compounds: a multifunctional nano-platform for advanced materials.

Polyoxometalates (POMs) are robust, discrete, and structurally well-defined metal-oxide cluster anions that have stimulated research in broad fields of science. Keplerates, as porous giant POMs, serve as a multifunctional nano-platform exhibiting fascinating chemical properties stemming from the porous molecular structure, substantial interior space, delocalization of d-electrons over the large molecular surface, . Consequently, Keplerates have attracted significant attention from scientists in the fields of chemistry, physics, biology, and materials sciences.

Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case.

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events.

Machine-learning-based adverse drug event prediction from observational health data: A review.

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning.

Early changes in soluble intracellular adhesion molecule-1 as prognostic biomarkers to immune checkpoint inhibitor.

Serologic biomarker to predict clinical outcome is needed for immune checkpoint inhibitors (ICIs). We evaluated soluble intercellular adhesion molecules-1 (sICAM-1) as a predictor of response to ICIs treatment. Ninety-five patients with cancer treated with ICI were studied.

Perspectives of data science in preclinical safety assessment.

The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling.

Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment.

Reduction of the rapid delayed rectifier potassium current ( ) drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes.

Circuit-level convergence of electronics and photonics: basic concepts and recent advances.

Integrated photonics is widely regarded as an important post-Moore's law research direction. However, it suffers from intrinsic limitations, such as lack of control and satisfactory photonic memory, that cannot be solved in the optical domain and must be combined with electronics for practical use. Inevitably, electronics and photonics will converge.

A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis.

Immune checkpoint inhibitors (ICIs), as a novel immunotherapy, are designed to modulate the immune system to attack malignancies. Despite their promising benefits, immune-related adverse events (IRAEs) may occur, and incidences are bound to increase with surging demand of this class of drugs in treating cancer. Myocarditis, although rare compared to other IRAEs, has a significantly higher fatal frequency.

Models of the cardiac L-type calcium current: A quantitative review.

The L-type calcium current ( ) plays a critical role in cardiac electrophysiology, and models of are vital tools to predict arrhythmogenicity of drugs and mutations. Five decades of measuring and modeling have resulted in several competing theories (encoded in mathematical equations). However, the introduction of new models has not typically been accompanied by a data-driven critical comparison with previous work, so that it is unclear which model is best suited for any particular application.

A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models.

Computational models of the electrical potential across a cell membrane are longstanding and vital tools in electrophysiology research and applications. These models describe how ionic currents, internal fluxes, and buffering interact to determine membrane voltage and form action potentials (APs). Although this relationship is usually expressed as a differential equation, previous studies have shown it can be rewritten in an algebraic form, allowing direct calculation of membrane voltage.

Translating pharmacology models effectively to predict therapeutic benefit.

Many in vitro and in vivo models are used in pharmacological research to evaluate the role of targeted proteins in a disease. Understanding the translational relevance and limitation of these models for analyzing a drug's disposition, pharmacokinetic/pharmacodynamic (PK/PD) profile, mechanism, and efficacy, is essential when selecting the most appropriate model of the disease of interest and predicting clinically efficacious doses of the investigational drug. Selected animal models used in ophthalmology, infectious diseases, oncology, autoimmune diseases, and neuroscience are reviewed here.

Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors.

Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation.