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. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

Dose evaluation of inter- and intra-fraction prostate motion in extremely hypofractionated intensity-modulated proton therapy for prostate cancer.

Inter- and intra-fractional prostate motion can deteriorate the dose distribution in extremely hypofractionated intensity-modulated proton therapy. We used verification CTs and prostate motion data calculated from 1024 intra-fractional prostate motion records to develop a voxel-wise based 4-dimensional method, which had a time resolution of 1 s, to assess the dose impact of prostate motion. An example of 100 fractional simulations revealed that motion had minimal impact on planning dose, the accumulated dose in 95 % of the scenarios fulfilled the clinical goals for target coverage (D95 > 37.5 Gy). This method can serve as a complementary measure in clinical setting to guarantee plan quality.

Characterization of a commercial bioluminescence tomography-guided system for pre-clinical radiation research.

BACKGROUND: Widely used Cone-beam computed tomography (CBCT)-guided irradiators have limitations in localizing soft tissue targets growing in a low-contrast environment. This hinders small animal irradiators achieving precise focal irradiation. PURPOSE: To advance image-guidance for soft tissue targeting, we developed a commercial-grade bioluminescence tomography-guided system (BLT, MuriGlo) for pre-clinical radiation research. We characterized the system performance and demonstrated its capability in target localization. We expect this study can provide a comprehensive guideline for the community in utilizing the BLT system for radiation studies. METHODS: MuriGlo consists of four mirrors, filters, lens, and charge-coupled device (CCD) camera, enabling a compact imaging platform and multi-projection and multi-spectral BLT. A newly developed mouse bed allows animals imaged in MuriGlo and transferred to a small animal radiation research platform (SARRP) for CBCT imaging and BLT-guided irradiation. Methods and tools were developed to evaluate the CCD response linearity, minimal detectable signal, focusing, spatial resolution, distortion, and uniformity. A transparent polycarbonate plate covering the middle of the mouse bed was used to support and image animals from underneath the bed. We investigated its effect on 2D Bioluminescence images and 3D BLT reconstruction accuracy, and studied its dosimetric impact along with the rest of mouse bed. A method based on pinhole camera model was developed to map multi-projection bioluminescence images to the object surface generated from CBCT image. The mapped bioluminescence images were used as the input data for the optical reconstruction. To account for free space light propagation from object surface to optical detector, a spectral derivative (SD) method was implemented for BLT reconstruction. We assessed the use of the SD data (ratio imaging of adjacent wavelength) in mitigating out of focusing and non-uniformity seen in the images. A mouse phantom was used to validate the data mapping. The phantom and an in vivo glioblastoma model were utilized to demonstrate the accuracy of the BLT target localization. RESULTS: The CCD response shows good linearity with < 0.6% residual from a linear fit. The minimal detectable level is 972 counts for 10 × 10 binning. The focal plane position is within the range of 13-18 mm above the mouse bed. The spatial resolution of 2D optical imaging is < 0.3 mm at Rayleigh criterion. Within the region of interest, the image uniformity is within 5% variation, and image shift due to distortion is within 0.3 mm. The transparent plate caused < 6% light attenuation. The use of the SD imaging data can effectively mitigate out of focusing, image non-uniformity, and the plate attenuation, to support accurate multi-spectral BLT reconstruction. There is < 0.5% attenuation on dose delivery caused by the bed. The accuracy of data mapping from the 2D bioluminescence images to CBCT image is within 0.7 mm. Our phantom test shows the BLT system can localize a bioluminescent target within 1 mm with an optimal threshold and only 0.2 mm deviation was observed for the case with and without a transparent plate. The same localization accuracy can be maintained for the in vivo GBM model. CONCLUSIONS: This work is the first systematic study in characterizing the commercial BLT-guided system. The information and methods developed will be useful for the community to utilize the imaging system for image-guided radiation research.

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. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.

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. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway 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 (I Kr) via 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. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Systematic mutagenesis of Polerovirus protein P0 reveals distinct and overlapping amino acid functions in Nicotiana glutinosa.

The protein P0 serves as the viral suppressor of RNA silencing (VSR) for poleroviruses, but elicits the hypersensitive response (HR) in specific Nicotiana species. We subjected P0 proteins from turnip yellows virus (P0Tu) and potato leafroll virus (P0PL) to serial deletion and performed extensive site-directed mutagenesis of P0Tu. Most deletions of the N-terminus and many substitution mutations disrupted both HR elicitation and VSR activity. Two conserved blocks of amino acid residues were found to be associated with HR. A double lysine to arginine substitution in HR-specific block 1 caused P0Tu to elicit a more robust HR. Conversely, deletion or mutation of block 2 in the C-terminus preserved VSR activity, but impaired HR elicitation, allowing virus escape from Nicotiana glutinosa resistance when expressed in the heterologous potato virus X vector. Our observations suggest that P0 residues responsible for suppressing RNA silencing and eliciting HR have overlapping, but distinct functions.

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

The L-type calcium current ( I CaL ) plays a critical role in cardiac electrophysiology, and models of I CaL are vital tools to predict arrhythmogenicity of drugs and mutations. Five decades of measuring and modeling I CaL 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. In this review, we describe and compare 73 published mammalian I CaL models and use simulated experiments to show that there is a large variability in their predictions, which is not substantially diminished when grouping by species or other categories. We provide model code for 60 models, list major data sources, and discuss experimental and modeling work that will be required to reduce this huge list of competing theories and ultimately develop a community consensus model of I CaL . This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Molecular and Cellular Physiology.

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. Due to the overwhelming complexity of the immune system, this condition is not well understood, despite the significant research efforts devoted to it. To better understand the development and progression of autoimmune myocarditis and the roles of ICIs therein, we suggest a new approach: mathematical modelling. Mathematical modelling of myocarditis has enormous potential to determine which parts of the immune system are critical to the development and progression of the disease, and therefore warrant further investigation. We provide the immunological background needed to develop a mathematical model of this disease and review relevant existing models of immunology that serve as the mathematical inspiration needed to develop this field.

Mobile bioluminescence tomography-guided system for pre-clinical radiotherapy research.

Due to low imaging contrast, a widely-used cone-beam computed tomography-guided small animal irradiator is less adept at localizing in vivo soft tissue targets. Bioluminescence tomography (BLT), which combines a model of light propagation through tissue with an optimization algorithm, can recover a spatially resolved tomographic volume for an internal bioluminescent source. We built a novel mobile BLT system for a small animal irradiator to localize soft tissue targets for radiation guidance. In this study, we elaborate its configuration and features that are indispensable for accurate image guidance. Phantom and in vivo validations show the BLT system can localize targets with accuracy within 1 mm. With the optimal choice of threshold and margin for target volume, BLT can provide a distinctive opportunity for investigators to perform conformal biology-guided irradiation to malignancy.

Quantitative molecular bioluminescence tomography.

SIGNIFICANCE: Bioluminescence imaging and tomography (BLT) are used to study biologically relevant activity, typically within a mouse model. A major limitation is that the underlying optical properties of the volume are unknown, leading to the use of a "best" estimate approach often compromising quantitative accuracy. AIM: An optimization algorithm is presented that localizes the spatial distribution of bioluminescence by simultaneously recovering the optical properties and location of bioluminescence source from the same set of surface measurements. APPROACH: Measured data, using implanted self-illuminating sources as well as an orthotopic glioblastoma mouse model, are employed to recover three-dimensional spatial distribution of the bioluminescence source using a multi-parameter optimization algorithm. RESULTS: The proposed algorithm is able to recover the size and location of the bioluminescence source while accounting for tissue attenuation. Localization accuracies of <1 mm are obtained in all cases, which is similar if not better than current "gold standard" methods that predict optical properties using a different imaging modality. CONCLUSIONS: Application of this approach, using in-vivo experimental data has shown that quantitative BLT is possible without the need for any prior knowledge about optical parameters, paving the way toward quantitative molecular imaging of exogenous and indigenous biological tumor functionality.

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. Rewriting in this form requires the introduction of a new parameter, called Γ0 in this manuscript, which represents the net concentration of all charges that influence membrane voltage but are not considered in the model. Although several studies have examined the impact of Γ0 on long-term stability and drift in model predictions, there has been little examination of its effects on model predictions, particularly when a model is refit to new data. In this study, we illustrate how Γ0 affects important physiological properties such as action potential duration restitution, and examine the effects of (in)correctly specifying Γ0 during model calibration. We show that, although physiologically plausible, the range of concentrations used in popular models leads to orders of magnitude differences in Γ0, which can lead to very different model predictions. In model calibration, we find that using an incorrect value of Γ0 can lead to biased estimates of the inferred parameters, but that the predictive power of these models can be restored by fitting Γ0 as a separate parameter. These results show the value of making Γ0 explicit in model formulations, as it forces modellers and experimenters to consider the effects of uncertainty and potential discrepancy in initial concentrations upon model predictions.

Deep-Tissue Activation of Photonanomedicines: An Update and Clinical Perspectives.

With the continued development of nanomaterials over the past two decades, specialized photonanomedicines (light-activable nanomedicines, PNMs) have evolved to become excitable by alternative energy sources that typically penetrate tissue deeper than visible light. These sources include electromagnetic radiation lying outside the visible near-infrared spectrum, high energy particles, and acoustic waves, amongst others. Various direct activation mechanisms have leveraged unique facets of specialized nanomaterials, such as upconversion, scintillation, and radiosensitization, as well as several others, in order to activate PNMs. Other indirect activation mechanisms have leveraged the effect of the interaction of deeply penetrating energy sources with tissue in order to activate proximal PNMs. These indirect mechanisms include sonoluminescence and Cerenkov radiation. Such direct and indirect deep-tissue activation has been explored extensively in the preclinical setting to facilitate deep-tissue anticancer photodynamic therapy (PDT); however, clinical translation of these approaches is yet to be explored. This review provides a summary of the state of the art in deep-tissue excitation of PNMs and explores the translatability of such excitation mechanisms towards their clinical adoption. A special emphasis is placed on how current clinical instrumentation can be repurposed to achieve deep-tissue PDT with the mechanisms discussed in this review, thereby further expediting the translation of these highly promising strategies.

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. Each area has specific challenges around translatability and determination of an efficacious dose: new patient-specific dosing methods may help overcome these limitations.

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. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.

Quantitative Bioluminescence Tomography for In Vivo Volumetric-Guided Radiotherapy.

Several groups, including ours, have initiated efforts to develop small-animal irradiators that mimic radiation therapy (RT) for human treatment. The major image modality used to guide irradiation is cone-beam computed tomography (CBCT). While CBCT provides excellent guidance capability, it is less adept at localizing soft tissue targets growing in a low image contrast environment. In contrast, bioluminescence imaging (BLI) provides strong image contrast and thus is an attractive solution for soft tissue targeting. However, commonly used 2D BLI on an animal surface is inadequate to guide irradiation, because optical transport from an internal bioluminescent tumor is highly susceptible to the effects of optical path length and tissue absorption and scattering. Recognition of these limitations led us to integrate 3D bioluminescence tomography (BLT) with the small animal radiation research platform (SARRP). In this chapter, we introduce quantitative BLT (QBLT) with the advanced capabilities of quantifying tumor volume for irradiation guidance. The detail of system components, calibration protocol, and step-by-step procedure to conduct the QBLT-guided irradiation are described.

Bioluminescence tomography system for in vivo irradiation guidance.

We constructed a bioluminescence tomography(BLT) to localize soft tissue targets for preclinical radiotherapy study. With the threshold and margin designed for target volume, BLT can provide opportunity to perform conformal irradiation to malignancy.

Ultra-sensitive single pixel bioluminescence tomography for in vivo cell tracking.

We presented a single-pixel bioluminescence tomography (SPBLT) to monitor single or few cells in live animal. Simulations are proposed to validate the capability of SPBLT in detecting weak bioluminescence signal emitted from cells in vivo.

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. The photonic fabrication and integration technology is gradually maturing and electronics-photonics convergence (EPC) is experiencing a transition from device integration to circuit design. We derive a conceptual framework consisting of regulator, oscillator, and memory for scalable integrated circuits based on the fundamental concepts of purposeful behavior in cybernetics, entropy in information theory, and symmetry breaking in physics. Leveraging this framework and emulating the successes experienced by electronic integrated circuits, we identify the key building blocks for the integrated circuits for EPC and review the recent advances.