Hepatocytes interact with new compounds: metabolism is a frequent consequence. Hepatocyte micromechanisms have evolved to adjust in real time to interact with any never-before-seen compound. To demonstrate understanding of those mechanisms, next
generation, pharmacologically useful hepatocyte models must strive to do the same: their micromechanisms recognize a compound as not previously seen and respond based on prior experience. How might that be accomplished when uncertainty is large and detailed data are chronically limited? My first goal was to answer that question: engineer a new class of hepatocyte models that draw on their own past experiences and compound physicochemical properties (molecular weight, pKa, logP, etc.) to respond uniquely to a new compound: the events that emerge during model execution provide a useful prediction of that compound's metabolic clearance. I used the new synthetic (combining elements to form a whole) method of modeling and simulation. The method involves building extant--actually existing and observable--working biomimetic micromechanisms. The micromechanisms within my successful in silico hepatocytes are comprised of autonomous, interactive objects and agents that map to six key components: extracellular media, cells, transporters, metabolic enzymes, cytosolic binding factors, and compounds. In silico clearance emerges from their interactions.
Within livers, hepatocytes are spatially organized into functional units called lobules. Those hepatocytes exhibit location dependent, possibly cooperative, properties, including gene expression and metabolic clearance. These properties frequently adapt to changes
in compound type and exposure. Drug-induced hepatocyte damage can also be location dependent. Such spatially heterogeneous phenomena are referred to as hepatic zonation. Next generation, pharmacologically useful, liver models must be capable of exhibiting
similar phenomena under analogous conditions. My second goal was to extend my new class of models to the level of hepatic lobules. I organized autonomous agents, which map to small units of lobular function, into structures called Zonally Responsive Lobular Analogues (ZoRLA). The micromechanisms within each that enabled achieving a degree of validation were given two tasks: protect hypothetical external "tissues" by eliminating simulated toxins and minimize resource consumption. The zonation patterns
that emerged are striking similarities to reported patterns. ZoRLA are designed to become components of future, virtual organisms.
