Virtual Patients and Digital Twins in the Systems Analysis of Drug Discovery and Development

This article collection highlights the growing impact of mathematical and computational modeling in the field of drug development and personalized medicine. The first article provides a comprehensive guide on constructing virtual clinical trials, emphasizing best practices in model design, parameter estimation, sensitivity, and cohort creation to support population-based analyses. The second article demonstrates the use of logical computational models to predict effective cancer drug combinations, showcasing the ability to efficiently identify and validate higher-order drug synergies beyond traditional pairwise approaches. The third article explores the concept of medical digital twins, outlining their origins in industry, the challenges posed by biological complexity, and a framework for applying digital twin technology to drug discovery and development. Finally, the fourth article applies mathematical modeling to CAR-T cell immunotherapy, offering insights into both therapeutic efficacy and potential side effects, particularly as they relate to the challenges of treating solid tumors and minimizing damage to healthy tissue. Together, these abstracts illustrate the transformative potential of computational tools to accelerate drug research, optimize therapies, and address complex medical challenges.
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It has become increasingly evident that the occurrence and progression of many major human diseases cannot not be simply attributed to one or two single molecular factors. Instead, human diseases tend to involve highly complex multiscale processes, which makes the traditional “one target-one intervention” trial-and-error approach inefficient and inconclusive in many scenarios, especially when target efficacy signals need to be translated from in vitro assays to in vivo scenarios and ultimately to human trials. To address this challenge, systems biology modeling has been proposed as valuable computational tools that can guide more accurate and efficient experimental designs during drug target identification and evaluation: by quantitatively describing the numerous set of biological interactions that make up the entire “system of interest”, it naturally goes beyond the target itself and that the potential systems-level impact on every relevant biological component and readout can all be captured and assessed. This can help researchers identify drug targets and therapeutics that are more likely to advance through preclinical testing, and expectedly this model-informed framework is gaining momentum in modern drug discovery.

On the other side, experiments in drug development always require significant time and resource commitment given the multiple rounds of efficacy and safety qualifications. Furthermore, “human experiments” (clinical trials) often cost millions, and unlike animal or cell experiments, clinical trials have much more rigorous ethical considerations and the outcomes are much more likely influenced by subject variability. Thus, systems pharmacology type modeling with the use of virtual patients and virtual trials is exceptional valuable in terms of prospectively simulating and predicting trial outcomes, so that unfavorable contributing factors can be mechanistically identified before the real trial starts to avoid unsuccessful translation. Significant progress has been made in this field during the past decade, along with the emergence of new data-driven techniques such as machine learning. This is also true for many novel treatment modalities such as multi-specific antibodies, XDCs, and cell/gene therapies, as new computational models have been developed and applied to make high-risk decisions in their preclinical to clinical translation.

Therefore, this research topic will focus on the development and application of novel systems-level approaches and modeling-based analyses in drug discovery and development, especially how such in silico approaches can demonstrate value and efficiency in the translational steps from multimodal data to target, from target to cell, from cell to animal, and from animal to human.

Topics of interest include but are not limited to:
• Systems pharmacology and pharmacometrics-based analysis of drug candidate efficacy, toxicity, and immunogenicity during preclinical and early phase clinical development
• Mechanistic modeling in translational medicine and precision dosing: the use of digital twins and virtual patients
• Methodology of digital twin and virtual patient/subject generation and analysis: variability, robustness, uncertainty, and predictivity
• Modeling investigations of emerging treatment modalities such as bi/tri/multi-specific antibodies, XDCs(X-drug conjugates), cytokine therapies, and cell/gene therapies

For this research topic, we welcome submissions of original research articles, in-depth reviews and mini-reviews.

Topic Editor Huilin Ma is employed by Bristol Myers Squibb. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

Keywords: Systems Biology, Quantitative Systems Pharmacology, In vitro-In vivo Correlation, Virtual Patient, Virtual Trial, Translational Modeling, Digital Twin, Model-informed Precision Dosing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.