Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Frontiers in Physiology: In silico oncology: a mechanistic multiscale model of clinical prostate cancer response to external radiation therapy as the core of a digital (virtual) twin. Sensitivity analysis and a clinical adaptation approach
Georgios Stamatakos et al
Abstract
Introduction: Prostate cancer (PCa) is the most frequent diagnosed malignancy in male patients in Europe and radiation therapy (RT) is a main treatment option. However, current RT concepts for PCa have an imminent need to be rectified in order to modify the radiotherapeutic strategy by considering (i) the personal PCa biology in terms of radio resistance and (ii) the individual preferences of each patient.
Methods: To this end, a mechanistic multiscale model of prostate tumor response to external radiotherapeutic schemes, based on a discrete entity and discrete event simulation approach has been developed. Following technical verification, an adaptation to clinical data approach is delineated. Multiscale data has been provided by the University of Freiburg. Additionally, a sensitivity analysis has been performed.
Results: The impact of model parameters such as cell cycle duration, dormant phase duration, apoptosis rate of living and progenitor cells, fraction of dormant stem and progenitor cells that reenter cell cycle, number of mitoses performed by progenitor cells before becoming differentiated, fraction of stem cells that perform symmetric division, fraction of cells entering the dormant phase following mitosis, alpha and beta parameters of the linear-quadratic model and oxygen enhancement ratio has been studied. The model has been shown to be particularly sensitive to the apoptosis rate of living stem and progenitor cells, the fraction of dormant stem and progenitor cells that reenter cell cycle, the fraction of stem cells that perform symmetric division and the fraction of cells entering the dormant phase following mitosis. A qualitative agreement of the model behavior with experimental and clinical knowledge has set the basis for the next steps towards its thorough clinical validation and its eventual certification and clinical translation. The paper showcases the feasibility, the fundamental design and the qualitative behavior of the model in the context of in silico experimentation.
Discussion: Further data is being collected in order to enhance the model parametrization and conduct extensive clinical validation. The envisaged digital twin or OncoSimulator, a concept and technologically integrated system that our team has previously developed for other cancer types, is expected to support both patient personalized treatment and in silico clinical trials.
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Computer Methods and Programs in Biomedicine: A porohyperelastic scheme targeted at High-Performance Computing frameworks for the simulation of the intervertebral disc
Dimitrios Lialios et al
Abstract
Background and objective: The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling of the intervertebral disc is a reliable porohyperelastic framework that captures the elaborate underlying mechanics. The increased complexity of such models requires significant computational power that is available within high-performance computing systems. The objective of this study is to present such a framework, validated both against literature and experiments, aiming to enable intervertebral disc research to benefit from state-of-the-art computational resources.
Methods: In the context of this work, we implement a biphasic model that captures the mechanical response of the intricate, tissue-dependent models of the solid phase along with the hydrostatic pressure effects of the fluid phase. The tissue-dependent models involve the hyperelastic ground substance, fibrillar reinforcement, and osmotic swelling. The derived porohyperelastic, staggered scheme is implemented in Alya, a finite element code targeted at high-performance computing applications. The formulation is subsequently verified and validated by comparing the results of consolidation simulations with literature data for simulations and experiments using either generic or patient-specific geometries. Additionally, in-house experiments are replicated, evaluating the model's ability to simulate alternating loading. Finally, the implementation's circadian response is compared to previous implementation of similar material models in commercial software.
Results: Results align well with experimental and literature findings in terms of disc height reduction (4% error), intradiscal pressure (14% error) and disc bulging. Validating the patient-specific geometry results in 4% and 7% deviation in measuring height loss. Simulations show excellent agreement with in-house experimental results, with less than 1% error regarding height reduction. Finally, the comparison to similar, published, earlier implementation in commercial software unveils excellent agreement of less than 1% error for the water content during circadian simulations. Simulation times are reported at 4 min per circadian cycle in the supercomputer Marenostrum V.
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Current Drug Discovery Technologies: In-Silico Approaches for Drug Designing Technology: Bridging Discovery and Development
Aminul Islam et al
Abstract
Traditional drug discovery processes have disadvantages such as efficiency, cost, and high attrition rates. In silico methods, involving computational simulations and modelling, offer powerful solutions to bridge the gap between discovery and development. This review explores various in silico approaches, including ligand-based and structure-based drug design, virtual screening, molecular docking, and ADMET prediction. We explore their utilization throughout different phases of phar-maceutical development, spanning from target identification and lead refinement to forecasting tox-icity and pharmacokinetics. In-silico methods enable rapid lead identification and optimization, re-ducing reliance on expensive wet lab experiments. They contribute to improved drug quality by pre-dicting ADMET properties and off-target effects, ultimately accelerating development timelines and lowering costs. In silico approaches are revolutionizing drug design by providing predictive and cost-effective solutions. Incorporating them into the design process streamlines lead refinement and en-hances the likelihood of success for potential drugs, ultimately expediting the translation of innova-tive treatments to patients.
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