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!
Annals of Biomedical Engineering: In Silico Clinical Trial for Osteoporosis Treatments to Prevent Hip
Giacomo Savelli et al
Abstract
Osteoporosis represents a major healthcare concern. The development of novel treatments presents challenges due to the limited cost-effectiveness of clinical trials and ethical concerns associated with placebo-controlled trials. Computational models for the design and assessment of biomedical products (In Silico Trials) are emerging as a promising alternative. In this study, a novel In Silico Trial technology (BoneStrength) was applied to replicate the placebo arms of two concluded clinical trials and its accuracy in predicting hip fracture incidence was evaluated. Two virtual cohorts (N = 1238 and 1226, respectively) were generated by sampling a statistical anatomy atlas based on CT scans of proximal femurs. Baseline characteristics were equivalent to those reported for the clinical cohorts. Fall events were sampled from a Poisson distribution. A multiscale stochastic model was implemented to estimate the impact force associated to each fall. Finite Element models were used to predict femur strength. Fracture incidence in 3 years follow-up was computed with a Markov chain approach; a patient was considered fractured if the impact force associated with a fall exceeded femur strength. Ten realizations of the stochastic process were run to reach convergence. Each realization required approximately 2500 FE simulations, solved using High-Performance Computing infrastructures. Predicted number of fractures was 12 ± 2 and 18 ± 4 for the two cohorts, respectively. The predicted incidence range consistently included the reported clinical data, although on average fracture incidence was overestimated. These findings highlight the potential of BoneStrength for future applications in drug development and assessment.
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Journal of Pharmaceutical Sciences: Qualification of Mechanistic Models in Biopharmaceutical Process Development
Till Briskot et al
Abstract
Mechanistic process models play an increasingly important role in biopharmaceutical process development and manufacturing in supporting process design, characterization, and informing process control strategies. Despite the potential of mechanistic models, there is currently no clear consensus or regulatory guideline on their qualification, i.e. the processes of determining whether a model is suitable to support decision making in process development. In this work, a systematic and risk-based qualification framework for mechanistic models in biopharmaceutical process development is introduced. The framework integrates key concepts from other modeling frameworks and guidelines such as the ASME V&V 40 published by the American Society of Mechanical Engineers (ASME) and preliminary considerations in process models published by Quality Innovation Group (QIG) of the European Medicines Agency (EMA). Key concepts of the proposed framework are discussed using two case studies, including a model-informed optimization of a biopharmaceutical ultrafiltration and diafiltration process and a model-informed control strategy of a chromatography polishing step. The suggested framework can act as a foundation for dialogue and guide for other modelers in biopharmaceutical process development. It holds the capability to harmonize modeling procedures throughout the industry and establish an agreement on the qualification of mechanistic models in biopharmaceutical process development.
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Computers Methods and Programs in Biomedicine: Computational hemodynamic indices to identify Transcatheter Aortic Valve Implantation degeneration
Luca Crugnola et al
Abstract
The study population comprises fourteen patients: seven cases with SVD at long-term follow-up were identified and seven cases without SVD were randomly extracted from the same cohort. Starting from pre-operative CT images, we created trustworthy post-TAVI scenarios by virtually inserting the bioprosthetic valve (stent and leaflets) and we qualitatively validated such virtual scenarios against post-TAVI CT scans, when available. We then performed numerical simulations imposing personalized inlet conditions based on patient-specific Echo Doppler cardiac output measurements and the numerical results were post-processed to identify suitable hemodynamics indices with the aim of discriminating between the SVD and non-SVD groups of patients. In particular, differences in terms of each individual index were evaluated using a Wilcoxon rank-sum test. Moreover, we defined three synthetic scores, based on suitably scaled hemodynamic indices of stress and vorticity, evaluated in different contexts: on the leaflets, in the ascending aorta, and in the whole domain.
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