Integrated mechanistic and data-driven simulation approaches for directly predicting spinal treatment outcomes during in silico clinical trials

While advances in medical technologies in the last decades have undoubtedly been impressive, these have come, especially in recent years, at ever-growing financial cost to society. The major source of this growth is the rapidly expanding cost of research & development processes, and the compounding effect of failures of new treatments and device designs at clinical trial stages - the latter effectively meaning that costs of failed devices and treatments must be rolled onto those of the smaller number of successful technologies. Moreover, inherent limitations of clinical trial processes themselves, such as their capacity to properly assess treatment efficacy over the long-term, or to expose devices to acceptable ranges of operating conditions and patient physiologies, mean that even "successful" designs may not perform as expected in practice; the need for further treatment in such cases adds to both the financial and personal costs.

A new paradigm, labelled In-silico Clinical Trials (ISCTs), has been proposed recently as a solution to this problem. ISCTs are pre-conventional trials that are performed entirely or in part using individualised computer simulations that model some aspect of drug effect, medical device, or clinical intervention. In other words, they are counterparts to conventional trials that test devices on large cohorts of virtual patients, rather than real ones. These virtual cohorts may be extremely large: hundreds of thousands, or even millions strong; and could enable far more rigorous testing of treatments/devices than is currently possible. If fully achieved, such a technology would substantially reduce the scale and duration of, and even partially replace expensive conventional trials, while vastly increasing the effectiveness of those trials that are undertaken.

A fundamental prerequisite of this vision is availability of effective computational modelling strategies that enable clinical outcomes of a treatment to be predicted with sufficient accuracy and reliability, and within acceptable timeframes. For orthopaedic devices, as examples, computational models based on rigorous physical principles, and solved using techniques such as finite element methods, can be used to compute the mechanical state of a prosthesis and surrounding tissues under physiological conditions. This in turn can be used to estimate the likelihood of device failure according to known failure modes (bone fracture, bone/device interface failure, device wear, etc.). The present project will focus on treatments for vertebral fractures, such as vertebroplasty and kyphoplasty, as exemplars. Within iMBE, and elsewhere, substantial effort has been devoted to development and validation of models in these domains.

But, their complexity means computation times are often high, and solution stability and reliability can also be problematic. ISCTs demand execution of large volumes of such simulations, which may be prohibitive. The central hypothesis of this project is that advanced data-driven modelling techniques (machine learning) can be used to identify relationships between computational model inputs (anatomy, treatment configuration, loads, tissue condition, etc.) and predicted treatment failure modes, and thereby to enable the expensive computational models to be circumvented. Moreover, if further related directly to lower-level data (e.g. images and other input signals, and subject metadata), they may even allow circumvention of parts of the pre-processing workflows as well.

The overarching aim of the project is to develop and validate novel data-driven modelling approaches that can predict incidence of treatment failure modes from basic inputs like treatment configuration, and patient anatomy and tissue properties.

Regenerative Medicine been defined as "an interdisciplinary approach, spanning tissue
engineering, stem cell biology, gene therapy, cellular therapeutics, biomaterials (scaffolds and matrices),nanoscience, bioengineering and chemical biology that seeks to repair or replace damaged or diseased human cells or tissues to restore normal function, (UK Strategy for Regenerative Medicine). CDT TERM will focus on acellular therapies, scaffolds,autologous cells and regenerative devices, which can be delivered to patients as class three device interventions, thus reducing the time and cost of translation and which provide an opportunity to deliver economic growth and benefits to health in the next decade. The primary beneficiaries of CDT TERM are patients, the health service, UK industry, as well as the academic community and the students themselves. Recognising that the impact and benefit from CDT TERM will arise in the future, the statements describing impact below are supported by evidence of actual impact from our existing research and training.

Patients will benefit from regenerative interventions, which address unmet clinical needs, have improved safety and reliability, have been stratified to meet patients needs and manufactured in a cost effective manner. An example of impact arising from previous students work is a new acellular scaffold for young adult heart valve repair, which has demonstrated improved clinical outcomes at five years.

The Health Service will benefit from collaborations on research, development and evaluation of technologies, through existing partnerships with National Health Service Blood and Transplant NHSBT and the Leeds Biomedical Musculoskeletal Research Unit LMBRU. NHSBT will benefit through collaborative projects, through technology transfer, through enhancement of manufacturing processes, through pre-clinical evaluation of products and supply of trained personnel. We currently collaborate on heart valves, skin, ligaments and arteries, have licensed patents on acellular bioprocesses, and support product and process developments with pre-clinical testing and simulation. LMBRU and NHS clinicians will benefits from our collaborative research and training environment and access to our research expertise, facilities and students. Existing collaborative projects include, delivery devices for minimally manipulated stem cells and applied imaging for early OA.

Industry will benefit from supply of highly trained multidisciplinary engineers and scientists, from collaborative research and development projects, from creation and translation of IP, creation of spinout companies and through access to unique equipment, facilities and expertise. We have demonstrated: successful spin outs in form of Tissue Regenix and Credentis; successful commercialisation of a novel biological scaffolds for vascular patch repair; sustainable long term R and D and successful licensing of technology with DePuy; collaborative research with Invibio, partnering with Simulation Solutions to develop new pre-clinical simulation systems, which been adopted by regulatory agencies such as China FDA. Our graduates and researchers are employed by our industry partners.

The academic community will benefit through collaborative research and access to our facilities. We have funded collaborations with over 30 academic institutions in UK and internationally. The CDT TERM will support these collaborations and the academic partners will support student research and training. The CDT students will benefit from enhanced integrated multidisciplinary training and research, a cohort experience focused on research innovation and translation, access to our research partners, industry and clinicians. Feedback from existing students has identified the benefit of the multidisciplinary experience, the depth and breadth of excellence in our research base, the outstanding facilities and the added value of the cohort training.