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Summary: Bile acid (BA) metabolism is a complex system that includes a wide variety of primary and secondary, as well as conjugated and unconjugated BAs that undergo continuous enterohepatic circulation (EHC). Alterations in both composition and dynamics of BAs have been associated with various diseases. However, a mechanistic understanding of the relationship between altered BA metabolism and related diseases is lacking. Computational modeling may support functional analyses of the physiological processes involved in the EHC of BAs along the gut-liver axis. In this study, we developed a physiologically based model of murine BA metabolism describing synthesis, hepatic and microbial transformations, systemic distribution, excretion, and EHC of BAs at the whole-body level. For model development, BA metabolism of specific pathogen-free (SPF) mice was characterized in vivo by measuring BA levels and composition in various organs, expression of transporters along the gut, and cecal microbiota composition. We found significantly different BA levels between male and female mice that could only be explained by adjusted expression of the hepatic enzymes and transporters in the model. Of note, this finding was in agreement with experimental observations. The model for SPF mice could also describe equivalent experimental data in germ-free mice by specifically switching off microbial activity in the intestine. The here presented model can therefore facilitate and guide functional analyses of BA metabolism in mice, e.g., the effect of pathophysiological alterations on BA metabolism and translation of results from mouse studies to a clinically relevant context through cross-species extrapolation.

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.