Optimizing Early Clinical Investigations in Cancer Immunotherapy: The Translational Journey of RG6292, a Novel, Selective Treg-Depleting Antibody.

The multifaceted IL-2/IL-2R biology and its modulation by promising therapeutic agents are highly relevant topics in the cancer immunotherapy field. A novel CD25-Treg-depleting antibody (Vopikitug, RG6292) has been engineered to preserve IL-2 signaling on effector T cells to enhance effector activation and antitumor immunity, and is currently being evaluated in the clinic. The Entry into Human-enabling framework described here investigated the characteristics of RG6292, from in vitro quantification of CD25 and RG6292 pharmacology using human tissues to in vivo assessment of PK/PD/safety relationships in cynomolgus monkeys as non-human primate species (NHP). Fundamental knowledge on CD25 and Treg biology in healthy and diseased tissues across NHP and human highlighted the commonalities between these species in regard to the target biology and demonstrated the conservation of RG6292 properties between NHP and human. The integration of in vitro and in vivo PK/PD/safety data from these species enabled the identification of human relevant safety risks, the selection of the most appropriate safe starting dose and the projection of the pharmacologically-relevant dose range. The first clinical data obtained for RG6292 in patients verified the appropriateness of the described approaches as well as validated the full clinical relevance of the projected safety, PK, and PD profiles from animal to man. This work shows how the integration of mechanistic non-clinical data increases the predictive value for human, allowing efficient transition of drug candidates and optimizations of early clinical investigations.

Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology.

The promise of transforming digital technologies into treatments is what drives the development of digital therapeutics (DTx), generally known as software applications embedded within accessible technologies-such as smartphones-to treat, manage, or prevent a pathological condition. Whereas DTx solutions that successfully demonstrate effectiveness and safety could drastically improve the life of patients in multiple therapeutic areas, there is a general consensus that generating therapeutic evidence for DTx presents challenges and open questions. We believe there are three main areas where the application of clinical pharmacology principles from the drug development field could benefit DTx development: the characterization of the mechanism of action, the optimization of the intervention, and, finally, its dosing. We reviewed DTx studies to explore how the field is approaching these topics and to better characterize the challenges associated with them. This leads us to emphasize the role that the application of clinical pharmacology principles could play in the development of DTx and to advocate for a development approach that merges such principles from development of traditional therapeutics with important considerations from the highly attractive and fast-paced world of digital solutions.

Introduction of an artificial neural network-based method for concentration-time predictions.

Pharmacometrics and the application of population pharmacokinetic (PK) modeling play a crucial role in clinical pharmacology. These methods, which describe data with well-defined equations and estimate physiologically interpretable parameters, have not changed substantially during the past decades. Although the methods have proven their usefulness, they are often resource intensive and require a high level of expertise. We investigated whether a method based on artificial neural networks (ANNs) may provide an alternative approach for the prediction of concentration-time curve to supplement the gold standard methods. In this work, we used simulated data to overcome the requirement for a large clinical training data set, implemented a pharmacologically reasonable network architecture to improve extrapolation to different dosing schemes, and used transfer learning to quickly adapt the predictions to new patient groups. We demonstrate that ANNs are able to learn the shape of concentration-time curves and make individual predictions based on a short sequence of PK measurements. Furthermore, an ANN trained on simulated data was applied to real clinical data and was demonstrated to extrapolate to different dosing schemes. We also adapted the ANN trained on simulated healthy subjects to simulated hepatic impaired patients through transfer learning. In summary, we demonstrate how ANNs could be leveraged in a PK workflow to efficiently make individual concentration-time predictions, and we discuss the current limitations and advantages of such an ANN-based method.

An in silico Model of T Cell Infiltration Dynamics Based on an Advanced in vitro System to Enhance Preclinical Decision Making in Cancer Immunotherapy.

Cancer immunotherapy often involves the use of engineered molecules to selectively bind and activate T cells located within tumour tissue. Fundamental to the success of such treatments is the presence or recruitment of T cells localised within the tumour microenvironment. Advanced organ-on-a-chip systems provide an in vitro setting in which to investigate how novel molecules influence the spatiotemporal dynamics of T cell infiltration into tissue, both in the context of anti-tumour efficacy and off-tumour toxicity. While highly promising, the complexity of these systems is such that mathematical modelling plays a crucial role in the quantitative evaluation of experimental results and maximising the mechanistic insight derived. We develop a mechanistic, mathematical model of a novel microphysiological in vitro platform that recapitulates T cell infiltration into epithelial tissue, which may be normal or transformed. The mathematical model describes the spatiotemporal dynamics of infiltrating T cells in response to chemotactic cytokine signalling. We integrate the model with dynamic imaging data to optimise key model parameters. The mathematical model demonstrates a good fit to the observed experimental data and accurately describes the distribution of infiltrating T cells. This model is designed to complement the in vitro system; with the potential to elucidate complex biological mechanisms, including the mode of action of novel therapies and the drivers of safety events, and, ultimately, improve the efficacy-safety profile of T cell-targeted cancer immunotherapies.

Loneliness in online students with disabilities: qualitative investigation for experience, understanding and solutions.

Online learning has given access to education for diverse populations including students with disabilities. In our university, the ratio of students with disabilities is substantially higher in the online programmes than face-to-face. Online learning provides high accessibility though it can result in a lonely experience. Accordingly, this study aimed to appraise the first-hand experience and understanding of loneliness in online students with disabilities (OSWD), and to discuss possible solutions. Thematic analysis on semi-structured interviews attended by nine OSWD identified: 'Self-paced study can reduce stigma but cause loneliness (Theme 1)', 'Loneliness and social difficulties relate to misunderstanding of disability (Theme 2)', and 'Activities, events and staff for informal socialisation are needed (Theme 3)'. As the demand for online learning is further expanded due to the current global pandemic, our findings will be helpful for online learning institutions worldwide to establish effective strategies to reduce loneliness in OSWD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41239-021-00301-x.

An Introduction to Machine Learning.

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level.

Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.