Development of an automatised platform for drug discovery in relevant human tissue models

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Brief description of the context of the research including potential impactsDrug discovery is known to be a long and expensive process with very low success outcomes. The whole process usually develops over three phases: i) synthesis of drug molecules, ii) preclinical animal trials and iii) human clinical trials. Preclinical animal trials have shown crucial limitations in the past years, as caused by the wide interspecies gaps between animals and humans. This often translates into wide unreliability in animal trials' outcomes, with huge losses in pharmaceutical research and development programs.The use of human-derived in vitro/ex vivo models has opened new horizons in this field, by preserving patients-specific features and reproducing physiological and pathological scenarios more reliably. The enhanced accessibility to biological and clinical data has led to the development of more sophisticated computational models and bioinformatics applications. Machine learning models have shown great potential in therapeutic applications as they can provide novel predictive insights into drug efficacy by processing a vast number of heterogeneous datasets. The combination of novel coupled in vitro-in silico models has been described to be the crucial direction to follow to improve current drug development pipelines as expressed by relevant regulatory authorities (e.g., FDA, EMA). The urgency in establishing novel therapeutic strategies is evident in the field of liver disease, encompassing a wide range of pathologies with different aetiological factors (e.g., viral infections, alcohol abuse, extensive high-fat diet, etc.). Orthotopic liver transplantation is still considered the gold standard treatment for end-stage liver disease. However, this practice is still affected by crucial negative aspects namely highlighted in the necessity of long-term immunosuppressive treatments and the current shortage of organs suitable for transplantation. This situation is further exacerbated by the dramatic paucity of therapeutic alternatives in several pathological scenarios (e.g., nonalcoholic steatohepatitis - NASH, and biliary atresia), posing a global challenge to identify and develop novel effective treatments.Aims and objectivesThe doctoral project concerns the development of an engineered process aimed at the derivation and testing of multiple reliable in vitro models (e.g., organoids, precision-cut tissue slices) from patients' surgical samples. These models will be further used to assess the efficacy of novel drug candidates compared to standard-of-care treatment, by employing computational models and informatics resources. Novelty of the research methodologyThe project aims to establish a multidisciplinary approach to tackle current shortcomings in drug discovery processes by adopting a multifaceted perspective. The activity encompasses three major areas of interest: biotechnology, engineering, and computer science. In this setting, the underlying clinical problem is not tackled by solely employing a conventional biology-based approach. However, the project aims to develop a multifaceted approach able to solve the clinical problem from a biological, engineering, and computer science perspective. If successful, this work will promote the establishment of novel drug development pipelines that can be further expanded to a wide array of pathological scenarios.Alignment to EPSRC's strategies and research areasThe project focuses on human clinical applications with the employment of human-derived samples. This fact limits the employment of animals in scientific research and complies with guidelines adopted by EPSRC. A consistent part of the doctoral project will concern the development of machine learning (ML) and artificial intelligence (AI) applications for drug development application. This work aligns with EPSRC's current interest in supporting ML/AI approaches for improving healthcare.No collaborators.