Physiologically Based Synthetic Models of Hepatic Disposition

Hunt, C. Anthony; Ropella, Glen E. P.; Li, Yan; Hung, Daniel Y.; Roberts, Michael S.

doi:10.1007/s10928-006-9031-3

Cited by 57 publications

(96 citation statements)

References 25 publications

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“…Our experimental approach followed an iterative sequence: RISL synthesis, testing and evaluation, validation or falsification, assessment, cogitation, and system revision. Hunt et al (2006) and Yan et al (2008a,b) describe an in silico liver composed of similarly constructed LOBULES. Because perfusate was recycled in (Lau et al, 2004), the data did not require that level of detail, so we designed, validated, and used a simpler in silico liver.…”

Section: Figmentioning

confidence: 99%

“…Together the five spaces function as a simplified analog of a hepatic lobule, or a portion thereof. As in Hunt et al (2006), we pooled or averaged results from n Monte Carlo LOBULE variants to represent an entire RISL. Drawing on results of early simulations, we decided that n Ն 6 was adequate for the objective (experimental results supporting that choice are provided in the supplemental material).…”

Section: Figmentioning

confidence: 99%

“…One was to simulate flow within the space (e.g., S1) representing perfusate, analogous to how it was done in Hunt et al (2006). S1 could be made circular and much larger than the HEPATIC spaces.…”

Section: Figmentioning

confidence: 99%

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Discovering Plausible Mechanistic Details of Hepatic Drug Interactions

Lam

Hunt²

2008

Drug Metab Dispos

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ABSTRACT:We sought a single set of mechanisms that could provide a quantitative explanation of three pairs of published time series data: perfusate concentration of digoxin and its metabolite in perfusates of isolated perfused rat livers 1) in the absence of any predose and with a predose of either 2) the uptake inhibitor rifampicin or 3) the efflux inhibitor quinidine. We used the synthetic modeling and simulation method because it provides a means of developing a scientific, experimental approach to unraveling and understanding some of the complexities of drug-drug interactions. We plugged together validated, quasi-autonomous software components to form abstract but mechanistically realistic analogs of livers undergoing perfusion [recirculating in silico livers (RISLs)], into which we could add objects representing each of the above three drugs, alone or in combination. Each RISL was a hypothesis about plausible mechanisms responsible for the referent time series data. Simulations tested each hypothesis. We used similarity measures (SMs) to compare results to the six sets of referent data. From many candidates, we identified an RISL having time-invariant mechanisms that achieved a weak SM (SM-1) but failed to achieve a stronger SM. Replacing four time-invariant with time-variant mechanisms along with addition of new enzyme and transporter components achieved the most stringent SM: simulated digoxin and metabolite perfusate levels were experimentally indistinguishable from the referent data for all three treatments. The mechanisms simulated unanticipated loss of hepatic viability during the original wet-lab experiments: erosion of hepatic accessibility and of enzyme and transporter activities.Given the variety of transporters and enzymes that can be involved, how can we enhance substantially our ability to confidently anticipate the consequences of hepatic drug-drug interactions in advance of costly wet-lab experiments? To do so, we need improved knowledge of multilevel spatiotemporal mechanistic details. We need new classes of models with capabilities beyond those of the current pharmacokinetic (PK) and pharmacodynamic variety, plus methods to more realistically represent critical spatiotemporal details, all within singlemodel systems. Finally, we must be able to explore realistic, concurrent, interaction consequences of two or more drugs when given key physicochemical properties, which is infeasible using current PK models. An essential first step in realizing those needs is to show a fine-grained, computational analog in which measures of simulated interactions of two drugs are experimentally indistinguishable from those of referent wet-lab experiments. When the analog's assembled components map realistically to hepatic components and the measured consequences of in silico and wet-lab mechanisms are indistinguishable, we can posit that the responsible mechanisms of both systems are similar, as diagrammed in Fig. 1. Achieving these important goals has been an objective of this project.The referent wet-lab exp...

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Section: Figmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

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Discovering Plausible Mechanistic Details of Hepatic Drug Interactions

Lam

Hunt²

2008

Drug Metab Dispos

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“…If that is not an intended model use, which was the case for Ito et al, then an equational model is the better choice. For additional contrasts between models of the ISID class and traditional equation-based models, see Hunt et al (2006). It is worth recalling that all models of biological systems are, by necessity, abstractions.…”

Section: Garmire and Huntmentioning

confidence: 99%

“…The recently introduced synthetic modeling method (Grant et al, 2006;Hunt et al, 2006;Garmire et al, 2007;Tang et al, 2007;Yan et al, 2007) is intended specifically to engineer models that can be useful for gaining mechanistic insight into complex biological phenomena such as intestinal absorption of compounds that are substrates for both Pgp and CYP3A4. Such models can be constructed consistent with current knowledge using validated components.…”

mentioning

confidence: 99%

In Silico Methods for Unraveling the Mechanistic Complexities of Intestinal Absorption: Metabolism-Efflux Transport Interactions

Garmire¹,

Hunt²

2008

Drug Metab Dispos

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ABSTRACT:We present a relatively simple, abstract, yet mechanistically realistic, in silico intestinal device (ISID). Its design enabled exploration of the mechanistic details of absorption for passively absorbed compounds that are also dual substrates of drug-metabolizing enzymes (CYP) and transporters (PGP), including P-glycoprotein. CYP and PGP, functioning as validated analogs of their referents, are autonomous software objects within the ISID. These and other autonomous objects were plugged together to form a device, an ISID, that represents intestinal features at different scales and levels of detail. Changes in proximal-to-distal levels of CYP and PGP are represented separately from the mechanisms that influence drug absorption and metabolism. Results for six proximal-to-distal CYP-PGP patterns are presented, along with results for different CYP/PGP ratios and amounts. We detected no CYP-PGP synergy. However, CYP-PGP antagonism was measured. Increasing the PGP/CYP ratio to 10 increased compound retention in the simulated lumen but did not increase total metabolism. Different proximal-to-distal CYP-PGP patterns, with and without simulated nonspecific, intracellular binding, had substantial effects on measures of absorption, metabolism, and metabolic extraction ratio within the ISID. The changes were due more to CYP than to PGP. The ISID represents a new class of models that is suitable for experimentation. It expands the repertoire of experimental methods for unraveling the mechanistic details of intestinal drug absorption and in anticipating the absorption consequences of drug interactions. To distinguish clearly in silico compounds and processes from corresponding intestinal structures and processes, we use small caps when referring to the former.

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Focus on the Fundamentals: Toward Better Therapeutic Index Prediction

2008

Drug Efficacy, Safety, and Biologics Discovery

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Physiologically Based Synthetic Models of Hepatic Disposition

Cited by 57 publications

References 25 publications

Discovering Plausible Mechanistic Details of Hepatic Drug Interactions

Discovering Plausible Mechanistic Details of Hepatic Drug Interactions

In Silico Methods for Unraveling the Mechanistic Complexities of Intestinal Absorption: Metabolism-Efflux Transport Interactions

Focus on the Fundamentals: Toward Better Therapeutic Index Prediction

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