Development and application of a multi-component 3-D in vitro model for predictive pharmacokinetics of environmental pharmaceuticals in fish

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BACKGROUND: Many medicinal drugs end up in the environment and the pharmaceutical industry is required to assess their risk to non-target organisms. Fish is the group of organisms of highest risk of harm from environmental pharmaceuticals. Thus, it important that uptake and effects in fish can be predicted. We have developed a realistic in vitro Fish Gill Cell System (FIGCS), derived from trout gill cells, to study uptake, excretion and toxicity of pollutants1,2,3. FIGCS are grown on semi-permeable culture inserts and tolerate culture in water apically while the basolateral compartment is kept in culture medium, representing the blood. This system can bring an 88% Reduction of the number of fish needed for testing for accumulation of chemicals, and savings in terms of time, space and costs. In previous BBSRC projects (BB/J500483/1; BB/K501177/1; BB/M009513/1) we found that pharmaceuticals are taken up by a combination of processes and used ML to derive molecular descriptors determining uptake4,5. Our data show that uptake cannot be predicted from the pKa alone and physiological explanations exist. Using ML trained on FIGCS data, we found that uptake of ionisable pharmaceuticals is also dependent on solubility, log D, and molecular weight4. It is also likely that water chemistry variables other than pH influence pharmacokinetics (PK)1.The combination of ML and FIGCS allow us to mechanistically understand and computationally model PK of compounds in fish rapidly. However, PK models benefit from data on multiple compartments, including target organs. Through an innovative BBSRC Case studentship (BB/J500483/1) we established that FIGCS can be co-cultured with liver spheroids. The proposed studentship will validate these co-cultures and use them to determine fish PK of model drugs and their dependency on structure and water chemistry. In this highly interdisciplinary project, the student will deploy FIGCS-liver organoid co-culture and analytical chemistry to measure uptake, distribution, metabolism and excretion for 60+ pharmaceuticals, using LC-MS. Suspect screening analysis will be performed on liver spheroid extracts to identify metabolites using our ML assisted full-scan high resolution mass spectrometry (HRMS) methods6,7. ML models will be built in R, Python and licenced platforms to determine FIGCS dependency on pharmaceutical properties and water chemistry by genetic feature selection algorithms and sensitivity analysis of each model to achieve consensus. Using measured data from above and from the literature, time-series type ML models will be developed for all compounds simultaneously for in silico PK prediction.