Prediction of the Liver Safety Profile of a First-in-Class Myeloperoxidase Inhibitor Using Quantitative Systems Toxicology Modeling.

The novel myeloperoxidase inhibitor verdiperstat was developed as a treatment for neuroinflammatory and neurodegenerative diseases. During development, a computational prediction of verdiperstat liver safety was performed using DILIsym v8A, a quantitative systems toxicology (QST) model of liver safety.A physiologically-based pharmacokinetic (PBPK) model of verdiperstat was constructed in GastroPlus 9.8, and outputs for liver and plasma time courses of verdiperstat were input into DILIsym. In vitro experiments measured the likelihood that verdiperstat would inhibit mitochondrial function, inhibit bile acid transporters, and generate reactive oxygen species (ROS); these results were used as inputs into DILIsym, with two alternate sets of parameters used in order to fully explore the sensitivity of model predictions. Verdiperstat dosing protocols up to 600 mg BID were simulated for up to 48 weeks using a simulated population (SimPops) in DILIsym.Verdiperstat was predicted to be safe, with only very rare, mild liver enzyme increases as a potential possibility in highly sensitive individuals. Subsequent Phase 3 clinical trials found that ALT elevations in the verdiperstat treatment group were generally similar to those in the placebo group. This validates the DILIsym simulation results and demonstrates the power of QST modeling to predict the liver safety profile of novel therapeutics.

Analyzing the Mechanisms Behind Macrolide Antibiotic-Induced Liver Injury Using Quantitative Systems Toxicology Modeling.

PURPOSE: Macrolide antibiotics are commonly prescribed treatments for drug-resistant bacterial infections; however, many macrolides have been shown to cause liver enzyme elevations and one macrolide, telithromycin, has been pulled from the market by its provider due to liver toxicity. This work seeks to assess the mechanisms responsible for the toxicity of macrolide antibiotics. METHODS: Five macrolides were assessed in in vitro systems designed to test for bile acid transporter inhibition, mitochondrial dysfunction, and oxidative stress. The macrolides were then represented in DILIsym, a quantitative systems pharmacology (QST) model of drug-induced liver injury, placing the in vitro results in context with each compound's predicted liver exposure and known biochemistry. RESULTS: DILIsym results suggest that solithromycin and clarithromycin toxicity is primarily due to inhibition of the mitochondrial electron transport chain (ETC) while erythromycin toxicity is primarily due to bile acid transporter inhibition. Telithromycin and azithromycin toxicity was not predicted by DILIsym and may be caused by mechanisms not currently incorporated into DILIsym or by unknown metabolite effects. CONCLUSIONS: The mechanisms responsible for toxicity can be significantly different within a class of drugs, despite the structural similarity among the drugs. QST modeling can provide valuable insight into the nature of these mechanistic differences.

Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug-induced liver injury.

The drug development industry faces multiple challenges in the realization of safe effective drugs. Computational modeling approaches can be used to support these efforts. One approach, mechanistic modeling, is new to the realm of drug safety. It holds the promise of not only predicting toxicity for novel compounds, but also illuminating the mechanistic underpinnings of toxicity. To increase the scientific community's familiarity with mechanistic modeling in drug safety, this article seeks to provide perspective on the type of data used, how they are used and where they are lacking. Examples are derived from the development of DILIsym(®) software, a mechanistic model of drug-induced liver injury (DILI). DILIsym(®) simulates the mechanistic interactions and events from compound administration through the progression of liver injury and regeneration. Modeling mitochondrial toxicity illustrates the type and use of in vitro data to represent biological interactions, as well as insights on key differences between in vitro and in vivo conditions. Modeling bile acid toxicity illustrates a case in which the over-arching mechanism is well accepted, but many mechanistic details are lacking. Modeling was used to identify measurements predicted to strongly impact toxicity. Finally, modeling innate immune responses illustrates the importance of time-series data, particularly in the presence of positive and negative feedback loops, as well as the need for data from different animal species for better translation. These concepts are germane to most mechanistic models, although the details will vary. The use of mechanistic models is expected to improve the rational design of new drugs.

Mechanistic investigation of liver injury induced by BMS-932481, an experimental ɣ-secretase modulator.

BMS-932481 was designed to modulate ɣ-secretase activity to produce shorter and less amyloidogenic peptides, potentially averting liabilities associated with complete enzymatic inhibition. Although it demonstrated the intended pharmacology in the clinic, BMS-932481 unexpectedly caused drug-induced liver injury (DILI) in a multiple ascending dose study characterized by dose- and exposure-dependence, delayed onset manifestation, and a high incidence of hepatocellular damage. Retrospective studies investigating the disposition and probable mechanisms of toxicity of BMS-932481 are presented here. These included a mass balance study in bile-duct-cannulated rats and a metabolite profiling study in human hepatocytes, which together demonstrated oxidative metabolism followed by biliary elimination as the primary means of disposition. Additionally, minimal protein covalent binding in hepatocytes and lack of bioactivation products excluded reactive metabolite formation as a probable toxicological mechanism. However, BMS-932481 and 3 major oxidative metabolites were found to inhibit the bile salt export pump (BSEP) and multidrug resistance protein 4 (MRP4) in vitro. Considering human plasma concentrations, the IC50 values against these efflux transporters were clinically meaningful, particularly in the high dose cohort. Active uptake into human hepatocytes in vitro suggested the potential for hepatic levels of BMS-932481 to be elevated further above plasma concentrations, enhancing DILI risk. Conversely, measures of mitochondrial functional decline in hepatocytes treated with BMS-932481 were minimal or modest, suggesting limited contributions to DILI. Collectively, these findings suggested that repeat administration of BMS-932481 likely resulted in high hepatic concentrations of BMS-932481 and its metabolites, which disrupted bile acid transport via BSEP and MRP4, elevating serum biomarkers of liver injury.

An analysis of N-acetylcysteine treatment for acetaminophen overdose using a systems model of drug-induced liver injury.

N-acetylcysteine (NAC) is the treatment of choice for acetaminophen poisoning; standard 72-h oral or 21-h intravenous protocols are most frequently used. There is controversy regarding which protocol is optimal and whether the full treatment course is always necessary. It would be challenging to address these questions in a clinical trial. We used DILIsym, a mechanistic simulation of drug-induced liver injury, to investigate optimal NAC treatment after a single acetaminophen overdose for an average patient and a sample population (n = 957). For patients presenting within 24 h of ingestion, we found that the oral NAC protocol preserves more hepatocytes than the 21-h intravenous protocol. In various modeled scenarios, we found that the 21-h NAC infusion is often too short, whereas the full 72-h oral course is often unnecessary. We found that there is generally a good correlation between the time taken to reach peak serum alanine aminotransferase (ALT) and the time taken to clear N-acetyl-p-benzoquinone imine (NAPQI) from the liver. We also found that the most frequently used treatment nomograms underestimate the risk for patients presenting within 8 h of overdose ingestion. V(max) for acetaminophen bioactivation to NAPQI was the most important variable in the model in determining interpatient differences in susceptibility. In conclusion, DILIsym predicts that the oral NAC treatment protocol, or an intravenous protocol with identical dosing, is superior to the 21-h intravenous protocol and ALT is the optimal available biomarker for discontinuation of the therapy. The modeling also suggests that modification of the current treatment nomograms should be considered.

In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsym™: a mechanistic, mathematical model of DILI.

Drug-induced liver injury (DILI) is not only a major concern for all patients requiring drug therapy, but also for the pharmaceutical industry. Many new in vitro assays and pre-clinical animal models are being developed to help screen compounds for the potential to cause DILI. This study demonstrates that mechanistic, mathematical modeling offers a method for interpreting and extrapolating results. The DILIsym™ model (version 1A), a mathematical representation of DILI, was combined with in vitro data for the model hepatotoxicant methapyrilene (MP) to carry out an in vitro to in vivo extrapolation. In addition, simulations comparing DILI responses across species illustrated how modeling can aid in selecting the most appropriate pre-clinical species for safety testing results relevant to humans. The parameter inputs used to predict DILI for MP were restricted to in vitro inputs solely related to ADME (absorption, distribution, metabolism, elimination) processes. MP toxicity was correctly predicted to occur in rats, but was not apparent in the simulations for humans and mice (consistent with literature). When the hepatotoxicity of MP and acetaminophen (APAP) was compared across rats, mice, and humans at an equivalent dose, the species most susceptible to APAP was not susceptible to MP, and vice versa. Furthermore, consideration of variability in simulated population samples (SimPops™) provided confidence in the predictions and allowed examination of the biological parameters most predictive of outcome. Differences in model sensitivity to the parameters were related to species differences, but the severity of DILI for each drug/species combination was also an important factor.

Comparing the Liver Safety Profiles of 4 Next-Generation CGRP Receptor Antagonists to the Hepatotoxic CGRP Inhibitor Telcagepant Using Quantitative Systems Toxicology Modeling.

Calcitonin gene-related peptide (CGRP) signaling inhibitors have shown efficacy in both the acute and preventive treatment of migraine. Telcagepant, a first-generation CGRP receptor antagonist, was effective but failed in clinical trials due to hepatotoxicity. Subsequently, although 4 next-generation CGRP receptor antagonists (rimegepant, zavegepant, atogepant, and ubrogepant) were being advanced into late-stage clinical trials, due to telcagepant's failure, more confidence in the liver safety of these compounds was needed. DILIsym v6A, a quantitative systems toxicology (QST) model of drug-induced liver injury (DILI), was used to model all 5 compounds and thus to compare the 4 next-generation CGRP receptor antagonists to telcagepant. In vitro experiments were performed to measure the potential for each compound to inhibit bile acid transporters, produce oxidative stress, and cause mitochondrial dysfunction. Physiologically based pharmacokinetic models were produced for each compound in order to appropriately estimate liver exposure. DILIsym predicted clinical elevations of liver enzymes and bilirubin for telcagepant, correctly predicting the observed DILI liability of the first-generation compound. By contrast, DILIsym predicted that each of the 4 next-generation compounds would be significantly less likely to cause DILI than telcagepant. Subsequent clinical trials have validated these predictions for each of the 4 compounds, and all 3 of the compounds submitted to FDA to date (rimegepant, ubrogepant, and atogepant) have since been approved by the FDA with no warning for hepatotoxicity. This work demonstrates the potential for QST modeling to prospectively differentiate between hepatotoxic and nonhepatotoxic molecules within the same class.

Investigating bile acid-mediated cholestatic drug-induced liver injury using a mechanistic model of multidrug resistance protein 3 (MDR3) inhibition.

Inhibition of the canalicular phospholipid floppase multidrug resistance protein 3 (MDR3) has been implicated in cholestatic drug-induced liver injury (DILI), which is clinically characterized by disrupted bile flow and damage to the biliary epithelium. Reduction in phospholipid excretion, as a consequence of MDR3 inhibition, decreases the formation of mixed micelles consisting of bile acids and phospholipids in the bile duct, resulting in a surplus of free bile acids that can damage the bile duct epithelial cells, i.e., cholangiocytes. Cholangiocytes may compensate for biliary increases in bile acid monomers via the cholehepatic shunt pathway or bicarbonate secretion, thereby influencing viability or progression to toxicity. To address the unmet need to predict drug-induced bile duct injury in humans, DILIsym, a quantitative systems toxicology model of DILI, was extended by representing key features of the bile duct, cholangiocyte functionality, bile acid and phospholipid disposition, and cholestatic hepatotoxicity. A virtual, healthy representative subject and population (n = 285) were calibrated and validated utilizing a variety of clinical data. Sensitivity analyses were performed for 1) the cholehepatic shunt pathway, 2) biliary bicarbonate concentrations and 3) modes of MDR3 inhibition. Simulations showed that an increase in shunting may decrease the biliary bile acid burden, but raise the hepatocellular concentrations of bile acids. Elevating the biliary concentration of bicarbonate may decrease bile acid shunting, but increase bile flow rate. In contrast to competitive inhibition, simulations demonstrated that non-competitive and mixed inhibition of MDR3 had a profound impact on phospholipid efflux, elevations in the biliary bile acid-to-phospholipid ratio, cholangiocyte toxicity, and adaptation pathways. The model with its extended bile acid homeostasis representation was furthermore able to predict DILI liability for compounds with previously studied interactions with bile acid transport. The cholestatic liver injury submodel in DILIsym accounts for several processes pertinent to bile duct viability and toxicity and hence, is useful for predictions of MDR3 inhibition-mediated cholestatic DILI in humans.

Simulation of micelle formation in the presence of solutes.

We investigate solute encapsulation by copolymer micelles by performing discontinuous molecular dynamics simulations on a model solute-copolymer-solvent system. In this paper, we detail the effect of system density, copolymer mole fraction, and hydrophobic interaction between copolymer head and solute on the encapsulation efficiency and phase behavior of the system. The relative hydrophobicity of solute and copolymer head units acts as a coupling parameter that determines whether the system encapsulates or the copolymer and solute aggregate separately. The presence of solute particles makes micelles form more easily than they would otherwise. Five different mesophases or morphologies are possible. The micelle-unimer transition that occurs in a solute-free copolymer-solvent system is, for moderately hydrophobic solute particles, replaced by a transition between a micelle phase and a morphology in which copolymers surround a large aggregate of solute particles. The best encapsulation occurs for highly hydrophobic solute particles where solutes are dispersed throughout the micelle's core. The manner in which our results might be used by experimentalists to improve the encapsulation behavior of drug-copolymer systems is discussed.

Mechanistic Investigations Support Liver Safety of Ubrogepant.

Small-molecule calcitonin gene-related peptide (CGRP) receptor antagonists have demonstrated therapeutic efficacy for the treatment of migraine. However, previously investigated CGRP receptor antagonists, telcagepant and MK-3207, were discontinued during clinical development because of concerns about drug-induced liver injury. A subsequent effort to identify novel CGRP receptor antagonists less likely to cause hepatotoxicity led to the development of ubrogepant. The selection of ubrogepant, following a series of mechanistic studies conducted with MK-3207 and telcagepant, was focused on key structural modifications suggesting that ubrogepant was less prone to forming reactive metabolites than previous compounds. The potential for each drug to cause liver toxicity was subsequently assessed using a quantitative systems toxicology approach (DILIsym) that incorporates quantitative assessments of mitochondrial dysfunction, disruption of bile acid homeostasis, and oxidative stress, along with estimates of dose-dependent drug exposure to and within liver cells. DILIsym successfully modeled liver toxicity for telcagepant and MK-3207 at the dosing regimens used in clinical trials. In contrast, DILIsym predicted no hepatotoxicity during treatment with ubrogepant, even at daily doses up to 1000 mg (10-fold higher than the approved clinical dose of 100 mg). These predictions are consistent with clinical trial experience showing that ubrogepant has lower potential to cause hepatotoxicity than has been observed with telcagepant and MK-3207.

Quantitative Systems Toxicology Analysis of In Vitro Mechanistic Assays Reveals Importance of Bile Acid Accumulation and Mitochondrial Dysfunction in TAK-875-Induced Liver Injury.

TAK-875 (fasiglifam), a GPR40 agonist in development for the treatment of type 2 diabetes (T2D), was voluntarily terminated in Phase III trials due to adverse liver effects. The potential mechanisms of TAK-875 toxicity were explored by combining in vitro experiments with quantitative systems toxicology (QST) using DILIsym, a mathematical representation of drug-induced liver injury. In vitro assays revealed that bile acid transporters were inhibited by both TAK-875 and its metabolite, TAK-875-Glu. Experimental data indicated that human bile salt export pump (BSEP) inhibition by TAK-875 was mixed whereas sodium taurocholate co-transporting polypeptide (NTCP) inhibition by TAK-875 was competitive. Furthermore, experimental data demonstrated that both TAK-875 and TAK-875-Glu inhibit mitochondrial electron transport chain (ETC) enzymes. These mechanistic data were combined with a physiologically based pharmacokinetic (PBPK) model constructed within DILIsym to estimate liver exposure of TAK-875 and TAK-875-Glu. In a simulated population (SimPops) constructed to reflect T2D patients, 16/245 (6.5%) simulated individuals developed alanine aminotransferase (ALT) elevations, an incidence similar to that observed with 200 mg daily dosing in clinical trials. Determining the mode of bile acid transporter inhibition (Ki) was critical to accurate predictions. In addition, simulations conducted on a sensitive subset of individuals (SimCohorts) revealed that when either BSEP or ETC inhibition was inactive, ALT elevations were not predicted to occur, suggesting that the two mechanisms operate synergistically to produce the observed clinical response. These results demonstrate how utilizing QST methods to interpret in vitro experimental results can lead to an improved understanding of the clinically relevant mechanisms underlying drug-induced toxicity.

Prediction of Safety Margin and Optimization of Dosing Protocol for a Novel Antibiotic using Quantitative Systems Pharmacology Modeling.

Elevations of liver enzymes have been observed in clinical trials with BAL30072, a novel antibiotic. In vitro assays have identified potential mechanisms for the observed hepatotoxicity, including electron transport chain (ETC) inhibition and reactive oxygen species (ROS) generation. DILIsym, a quantitative systems pharmacology (QSP) model of drug-induced liver injury, has been used to predict the likelihood that each mechanism explains the observed toxicity. DILIsym was also used to predict the safety margin for a novel BAL30072 dosing scheme; it was predicted to be low. DILIsym was then used to recommend potential modifications to this dosing scheme; weight-adjusted dosing and a requirement to assay plasma alanine aminotransferase (ALT) daily and stop dosing as soon as ALT increases were observed improved the predicted safety margin of BAL30072 and decreased the predicted likelihood of severe injury. This research demonstrates a potential application for QSP modeling in improving the safety profile of candidate drugs.

The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury.

Idiosyncratic drug-induced liver injury (iDILI) is a serious concern in drug development. The rarity and multifactorial nature of iDILI makes it difficult to predict and explain. Recently, human leukocyte antigen (HLA) allele associations have provided strong support for a role of an adaptive immune response in the pathogenesis of many iDILI cases; however, it is likely that an adaptive immune attack requires several preceding events. Quantitative systems pharmacology (QSP), an in silico modeling technique that leverages known physiology and the results of in vitro experiments in order to make predictions about how drugs affect biological processes, is proposed as a potentially useful tool for predicting and explaining critical events that likely precede immune-mediated iDILI, as well as the immune attack itself. DILIsym, a QSP platform for drug-induced liver injury, has demonstrated success in predicting the presence of delayed hepatocellular stress events that likely precede the iDILI cascade, and has successfully predicted hepatocellular stress likely underlying iDILI attributed to troglitazone and tolvaptan. The incorporation of a model of the adaptive immune system into DILIsym would represent and important advance. In summary, QSP methods can play a key role in the future prediction and understanding of both immune-mediated and non-immune-mediated iDILI.

Application of a Mechanistic Model to Evaluate Putative Mechanisms of Tolvaptan Drug-Induced Liver Injury and Identify Patient Susceptibility Factors.

Tolvaptan is a selective vasopressin V2 receptor antagonist, approved in several countries for the treatment of hyponatremia and autosomal dominant polycystic kidney disease (ADPKD). No liver injury has been observed with tolvaptan treatment in healthy subjects and in non-ADPKD indications, but ADPKD clinical trials showed evidence of drug-induced liver injury (DILI). Although all DILI events resolved, additional monitoring in tolvaptan-treated ADPKD patients is required. In vitro assays identified alterations in bile acid disposition and inhibition of mitochondrial respiration as potential mechanisms underlying tolvaptan hepatotoxicity. This report details the application of DILIsym software to determine whether these mechanisms could account for the liver safety profile of tolvaptan observed in ADPKD clinical trials. DILIsym simulations included physiologically based pharmacokinetic estimates of hepatic exposure for tolvaptan and2 metabolites, and their effects on hepatocyte bile acid transporters and mitochondrial respiration. The frequency of predicted alanine aminotransferase (ALT) elevations, following simulated 90/30 mg split daily dosing, was 7.9% compared with clinical observations of 4.4% in ADPKD trials. Toxicity was multifactorial as inhibition of bile acid transporters and mitochondrial respiration contributed to the simulated DILI. Furthermore, simulation analysis identified both pre-treatment risk factors and on-treatment biomarkers predictive of simulated DILI. The simulations demonstrated that in vivo hepatic exposure to tolvaptan and the DM-4103 metabolite, combined with these 2 mechanisms of toxicity, were sufficient to account for the initiation of tolvaptan-mediated DILI. Identification of putative risk-factors and potential novel biomarkers provided insight for the development of mechanism-based tolvaptan risk-mitigation strategies.

Sandwich-Cultured Hepatocytes as a Tool to Study Drug Disposition and Drug-Induced Liver Injury.

Sandwich-cultured hepatocytes (SCH) are metabolically competent and have proper localization of basolateral and canalicular transporters with functional bile networks. Therefore, this cellular model is a unique tool that can be used to estimate biliary excretion of compounds. SCH have been used widely to assess hepatobiliary disposition of endogenous and exogenous compounds and metabolites. Mechanistic modeling based on SCH data enables estimation of metabolic and transporter-mediated clearances, which can be used to construct physiologically based pharmacokinetic models for prediction of drug disposition and drug-drug interactions in humans. In addition to pharmacokinetic studies, SCH also have been used to study cytotoxicity and perturbation of biological processes by drugs and hepatically generated metabolites. Human SCH can provide mechanistic insights underlying clinical drug-induced liver injury (DILI). In addition, data generated in SCH can be integrated into systems pharmacology models to predict potential DILI in humans. In this review, applications of SCH in studying hepatobiliary drug disposition and bile acid-mediated DILI are discussed. An example is presented to show how data generated in the SCH model were used to establish a quantitative relationship between intracellular bile acids and cytotoxicity, and how this information was incorporated into a systems pharmacology model for DILI prediction.

Exploring BSEP inhibition-mediated toxicity with a mechanistic model of drug-induced liver injury.

Inhibition of the bile salt export pump (BSEP) has been linked to incidence of drug-induced liver injury (DILI), presumably by the accumulation of toxic bile acids in the liver. We have previously constructed and validated a model of bile acid disposition within DILIsym®, a mechanistic model of DILI. In this paper, we use DILIsym® to simulate the DILI response of the hepatotoxic BSEP inhibitors bosentan and CP-724,714 and the non-hepatotoxic BSEP inhibitor telmisartan in humans in order to explore whether we can predict that hepatotoxic BSEP inhibitors can cause bile acid accumulation to reach toxic levels. We also simulate bosentan in rats in order to illuminate potential reasons behind the lack of toxicity in rats compared to the toxicity observed in humans. DILIsym® predicts that bosentan, but not telmisartan, will cause mild hepatocellular ATP decline and serum ALT elevation in a simulated population of humans. The difference in hepatotoxic potential between bosentan and telmisartan is consistent with clinical observations. However, DILIsym® underpredicts the incidence of bosentan toxicity. DILIsym® also predicts that bosentan will not cause toxicity in a simulated population of rats, and that the difference between the response to bosentan in rats and in humans is primarily due to the less toxic bile acid pool in rats. Our simulations also suggest a potential synergistic role for bile acid accumulation and mitochondrial electron transport chain (ETC) inhibition in producing the observed toxicity in CP-724,714, and suggest that CP-724,714 metabolites may also play a role in the observed toxicity. Our work also compares the impact of competitive and noncompetitive BSEP inhibition for CP-724,714 and demonstrates that noncompetitive inhibition leads to much greater bile acid accumulation and potential toxicity. Our research demonstrates the potential for mechanistic modeling to contribute to the understanding of how bile acid transport inhibitors cause DILI.

Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches.

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of "toxicity pathways" is described in the context of the 2007 US National Academies of Science report, "Toxicity testing in the 21st Century: A Vision and A Strategy." Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) - a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular "virtual tissue" model of the liver lobule that combines molecular circuits in individual hepatocytes with cell-cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.

Encapsulation Efficiency and Micellar Structure of Solute-Carrying Block Copolymer Nanoparticles.

We use discontinuous molecular dynamics (DMD) computer simulation to investigate the encapsulation efficiency and micellar structure of solute-carrying block copolymer nanoparticles as a function of packing fraction, polymer volume fraction, solute mole fraction, and the interaction parameters between the hydrophobic head blocks and between the head and the solute. The encapsulation efficiency increases with increasing polymer volume fraction and packing fraction but decreases with increasing head-head interaction strength. The latter is due to an increased tendency for the solute to remain on the micelle surface. We compared two different nanoparticle assembly methods, one in which the solute and copolymer co-associate and the other in which the copolymer micelle is formed before the introduction of solute. The assembly method does not affect the encapsulation efficiency but does affect the solute uptake kinetics. Both head-solute interaction strength and head-head interaction strength affect the density profile of the micelles; increases in the former cause the solute to distribute more evenly throughout the micelle, while increases in the latter cause the solute to concentrate further from the center of the micelle. We explain our results in the context of a model of drug insertion into micelles formulated by Kumar and Prud'homme; as conditions become more conducive to micelle formation, a stronger energy barrier to solute insertion forms which in turn decreases the encapsulation efficiency of the system.