In vitro assessment of farnesoid X receptor antagonism to predict drug-induced liver injury risk.

Drug-induced liver injury (DILI) continues to be a major cause of drug attrition and restrictive labeling. Given the importance of farnesoid X receptor (FXR) in bile acid homeostasis, drug-related FXR antagonism may be an important mechanism of DILI. However, a comprehensive assessment of this phenomenon broadly in the context of DILI is lacking. As such, we used an orthogonal approach comprising a FXR target gene assay in primary human hepatocytes and a commercially available FXR reporter assay to investigate the potential FXR antagonistic effects of an extensive test set of 159 compounds with and without association with clinical DILI. Data were omitted from analysis based on the presence of cytotoxicity to minimize false positive assay signals and other complications in data interpretation. Based on the experimental approaches employed and corresponding data, the prevalence of FXR antagonism was relatively low across this broad DILI test set, with 16-24% prevalence based on individual assay results or combined signals in both assays. Moreover, FXR antagonism was not highly predictive for identifying clinically relevant hepatotoxicants retrospectively, where FXR antagonist classification alone had minimal to moderate predictive value as represented by positive and negative likelihood ratios of 2.24-3.84 and 0.72-0.85, respectively. The predictivity did not increase significantly when considering only compounds with high clinical exposure (maximal or efficacious plasma exposures > 1.0 µM). In contrast, modest gains in predictive value of FXR antagonism were observed considering compounds that also inhibit bile salt export pump. In addition, we have identified novel FXR antagonistic effects of well-studied hepatotoxic drugs, including bosentan, tolcapone and ritonavir. In conclusion, this work represents a comprehensive evaluation of FXR antagonism in the context of DILI, including its overall predictivity and challenges associated with detecting this phenomenon in vitro.

A Novel Depurination Methodology to Assess DNA Alkylation of Chloro-Bis-Seco-Cyclopropylbenzoindoles Allowed for Comparison of Minor-Groove Reactivity.

Duocarmycins [including cyclopropyl pyrroloindole (CPI) or cyclopropyl benzoindole (CBI)] are a class of DNA minor-groove alkylators and seco-CPI/CBIs are synthetic pro-forms that can spirocyclize to CPI/CBI. Bis-CPI/CBIs are potential drug candidates because of their enhanced cytotoxicity from DNA crosslinking, but it is difficult to analyze them for structure-activity correlation because of their DNA reactivity. To study their DNA alkylation, neutral thermal hydrolysis has been frequently applied to process depurination. However, unwanted side reactions under this condition have been reported, which could lead to poor correlation of DNA alkylation data with efficacy results, especially for bis-CPI/CBIs. In this study, an acidic depurination method was developed and applied for analysis of DNA alkylation and shown to be an easier and milder method than the traditional neutral thermal hydrolysis. DNA alkylation and stability of three bis-seco-CBIs were characterized in comparison with two mono-seco-CPIs. The results suggested that: 1) The acidic depurination method was capable of capturing a more representative population, sometimes a different population, of DNA adducts as they existed on DNA compared with the heat depurination method. 2) Di-adenine adducts were captured as expected for the CBI dimers, although the major type of adduct was still mono-adenine adducts. 3) The rate of DNA alkylation, DNA adduct profile, and relative amounts of di-adduct versus mono-adduct were significantly affected by the size, and possibly lipophilicity, of the nonalkylating part of the molecules. 4) Spirocyclization and amide hydrolysis represented two major pathways of degradation. Overall, by applying acidic depurination analyses, this study has illustrated DNA adduct characteristics of novel bis-seco-CBIs with dominating mono-alkylation and provides an alternative method for evaluating DNA minor-groove alkylators. These findings provide an effective analytical tool to evaluate DNA alkylators and to study the DNA alkylation that is a disposition mechanism of these compounds.

Targeted LC-MS/MS Proteomics-Based Strategy To Characterize in Vitro Models Used in Drug Metabolism and Transport Studies.

Subcellular fractionation of tissue homogenate provides enriched in vitro models (e.g., microsomes, cytosol, or membranes), which are routinely used in the drug metabolism or transporter activity and protein abundance studies. However, batch-to-batch or interlaboratory variability in the recovery, enrichment, and purity of the subcellular fractions can affect performance of in vitro models leading to inaccurate in vitro to in vivo extrapolation (IVIVE) of drug clearance. To evaluate the quality of subcellular fractions, we developed a simple, targeted, and sensitive LC-MS/MS proteomics-based strategy, which relies on determination of protein markers of various cellular organelles, i.e., plasma membrane, cytosol, nuclei, mitochondria, endoplasmic reticulum (ER), lysosomes, peroxisomes, cytoskeleton, and exosomes. Application of the quantitative proteomics method confirmed a significant effect of processing variables (i.e., homogenization method and centrifugation speed) on the recovery, enrichment, and purity of isolated proteins in microsomes and cytosol. Particularly, markers of endoplasmic reticulum lumen and mitochondrial lumen were enriched in the cytosolic fractions as a result of their release during homogenization. Similarly, the relative recovery and composition of the total membrane fraction isolated from cell vs tissue samples was quantitatively different and should be considered in IVIVE. Further, analysis of exosomes isolated from sandwich-cultured hepatocyte media showed the effect of culture duration on compositions of purified exosomes. Therefore, the quantitative proteomics-based strategy developed here can be applied for efficient and simultaneous determination of multiple protein markers of various cellular organelles when compared to antibody- or activity-based assays and can be used for quality control of subcellular fractionation procedures including in vitro model development for drug metabolism and transport studies.

Evaluation of Time Dependent Inhibition Assays for Marketed Oncology Drugs: Comparison of Human Hepatocytes and Liver Microsomes in the Presence and Absence of Human Plasma.

PURPOSE: To evaluate an alternative in vitro system which can provide more quantitatively accurate drug drug interaction (DDI) prediction for 10 protein kinase inhibitors for which DDI risk was over-predicted by inhibition data generated in human liver microsomes (HLM). METHODS: Three cryopreserved human hepatocyte (hHEP) systems: 1) plated hHEPs; 2) hHEPs suspended in Dulbecco's Modified Eagle Medium (DMEM) and 3) hHEPs suspended in human plasma (plasma hHEPs) were developed to detect CYP3A time dependent inhibition, and the static mechanistic model was used to predict clinical outcomes. RESULTS: A general trend was observed in the CYP3A inactivation potency (k inact /K I, app ) as HLM > plated > DMEM ≥ plasma hHEPs. Using the static mechanistic model, DDIs predicted using parameters estimated from plated, DMEM and plasma hHEPs had 84, 74 and 95% accuracy (out of 19 clinical interaction studies) within 2-fold of the reported interaction, respectively. They demonstrated significant improvement compared to the DDIs predicted using parameters estimated from HLMs where 58% accuracy was obtained. CONCLUSIONS: Based on 19 DDIs, plasma hHEPs demonstrate a more reliable clinical DDI prediction for 10 protein kinase inhibitors and prototypical CYP3A time dependent inhibitors.

GTP1 metabolic stability assessment: A study of the tau PET tracer [18F]GTP1.

Tau PET imaging using the tau specific PET tracer [18F]GTP1 has been and is part of therapeutic trials in Alzheimer's disease to monitor the accumulation of tau aggregates in the brain. Herein, we examined the metabolic processes of GTP1 and assessed the influence of smoking on its metabolism through in vitro assays. The tracer metabolic profile was assessed by incubating GTP1 with human liver microsomes (HLM) and human hepatocytes. Since smoking strongly stimulates the CYP1A2 enzyme activity, we incubated GTP1 with recombinant CYP1A2 to evaluate the role of the enzyme in tracer metabolism. It was found that GTP1 could form up to eleven oxidative metabolites with higher polarity than the parent. Only a small amount (2.6 % at 60 min) of a defluorinated metabolite was detected in HLM and human hepatocytes incubations highlighting the stability of GTP1 with respect to enzymatic defluorination. Moreover, the major GTP1 metabolites were not the product of CYP1A2 activity suggesting that smoking may not impact in vivo tracer metabolism and subsequently GTP1 brain kinetics.

Second generation 2-pyridyl biphenyl amide inhibitors of the hedgehog pathway.

Potent and efficacious inhibitors of the hedgehog pathway for the treatment of cancer have been prepared using the 2-pyridyl biphenyl amide scaffold common to the clinical lead GDC-0449. Analogs with polar groups in the para-position of the aryl amide ring optimized potency, had minimal CYP inhibition, and possessed good exposure in rats. Compounds 9d and 14f potently inhibited hedgehog signaling as measured by Gli1 mRNA and were found to be equivalent or more potent than GDC-0449, respectively, when studied in a Ptch(+/-) medulloblastoma allograft model, that is, highly dependent on hedgehog signaling.

GDC-0449-a potent inhibitor of the hedgehog pathway.

SAR for a wide variety of heterocyclic replacements for a benzimidazole led to the discovery of functionalized 2-pyridyl amides as novel inhibitors of the hedgehog pathway. The 2-pyridyl amides were optimized for potency, PK, and drug-like properties by modifications to the amide portion of the molecule resulting in 31 (GDC-0449). Amide 31 produced complete tumor regression at doses as low as 12.5mg/kg BID in a medulloblastoma allograft mouse model that is wholly dependent on the Hh pathway for growth and is currently in human clinical trials, where it is initially being evaluated for the treatment of BCC.

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification.

BACKGROUND: There is a continued need for improvements in the efficiency of metabolite structure elucidation. OBJECTIVE: We propose to take LC Retention Time (RT) into consideration during the process of structure determination. METHODS: Herein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a predictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature examples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransformation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding). RESULTS: The CHI system for retention indexing is shown to be practical and simple to implement. A database of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an additional 17 datasets from the literature. Application of this database is illustrated. CONCLUSION: In our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.

Minimal physiologically-based pharmacokinetic modeling of DSTA4637A, A novel THIOMAB™ antibody antibiotic conjugate against Staphylococcus aureus, in a mouse model.

DSTA4637A, a THIOMAB™ antibody-antibiotic conjugate targeting Staphylococcus aureus, has shown promising bactericidal activity in a mouse model. DSTA4637A consists of a monoclonal anti-S. aureus antibody with an average of two rifalogue antibiotic molecules, dmDNA31, linked to its light chains. The goal of this study was to develop a minimal physiologically-based pharmacokinetic (mPBPK) model to characterize the pharmacokinetic (PK) properties of three analytes of DSTA4637A (i.e., total antibody, antibody-conjugated dmDNA31, and unconjugated dmDNA31) in mice, and to predict pharmacokinetics of DSTA4637A analytes in humans, as well as to provide an initial assessment for potential PK drug-drug interactions (DDI) in clinical trials via cross-species scaling of the mPBPK model. In the proposed model, selected organs, including heart, liver, and kidney, were connected anatomically with plasma and lymph flows. Mouse plasma and tissue concentrations of the three analytes of DSTA4637A were fitted simultaneously to estimate the PK parameters. Cross-species scaling of the model was performed by integrating allometric scaling and human physiological parameters. The final mPBPK model was able to successfully capture PK profiles of three DSTA4637A analytes in mouse plasma and in investigated organs. The model predicted a steady-state peak unbound dmDNA31 concentration lower than 5% of the IC50 of dmDNA31 towards cytochrome P450 following 100 mg/kg weekly intravenous dose, which suggests a low risk of PK DDI in humans for DSTA4637A with co-administered cytochrome P450 substrates. The proposed mPBPK modeling and cross-species scaling approaches provide valuable tools that facilitate the understanding and translation of DSTA4637A disposition from preclinical species to humans.

Mixed Matrix Method Provides A Reliable Metabolite Exposure Comparison for Assessment of Metabolites in Safety Testing (MIST).

BACKGROUND: The regulatory guidances on metabolites in safety testing (MIST) by US Food and Drug Administration (FDA) and International Conference on Harmonisation (ICH) describe the necessity to assess exposures of major circulating metabolites in humans at steady state relative to exposures achieved in nonclinical safety studies prior to the initiation of large scale clinical trials. This comparison can be accomplished by measuring metabolite concentrations in animals and humans with validated bioanalytical methods. However, bioanalysis of metabolites in multiple species and multiple studies is resource intensive and may impact the timelines of clinical studies. METHOD: A simple, reliable and accurate method has been developed for quantitative assessment of metabolite coverage in preclinical safety species by mixing equal volume of human plasma with blank plasma of animal species and vice versa followed by an analysis using LC-SRM or LC-HRMS. Here, we explored the reliability and accuracy of this method in several development projects at Genentech and compared the results to those obtained from validated bioanalytical methods. RESULTS: The mixed-matrix method provided comparable accuracy (within ±20%) to those obtained from validated bioanalysis but does not require authentic standards or radiolabeled compounds, which could translate to time and resource savings in drug development. CONCLUSION: Quantitative assessment of metabolite coverage in safety species can be made using mixed matrix method with similar accuracy and scientific rigor to those obtained from validated bioanalytical methods. Moving forward, we are encouraging the industry and regulators to consider accepting the mixed matrix method for assessing metabolite exposure comparisons between humans and animal species used in toxicology studies.

Comparison of metabolic soft spot predictions of CYP3A4, CYP2C9 and CYP2D6 substrates using MetaSite and StarDrop.

Metabolite identification study plays an important role in determining the sites of metabolic liability of new chemical entities (NCEs) in drug discovery for lead optimization. Here we compare the two predictive software, MetaSite and StarDrop, available for this purpose. They work very differently but are used to predict the site of oxidation by major human cytochrome P450 (CYP) isoforms. Neither software can predict non-CYP catalyzed metabolism nor the rates of metabolism. For the purpose of comparing the two software packages, we tested known probe substrate for these enzymes, which included 12 substrates of CYP3A4 and 18 substrates of CYP2C9 and CYP2D6 were analyzed by each software and the results were compared. It is possible that these known substrates were part of the training set but we are not aware of it. To assess the performance of each software we assigned a point system for each correct prediction. The total points assigned for each CYP isoform experimentally were compared as a percentage of the total points assigned theoretically for the first choice prediction for all substrates for each isoform. Our results show that MetaSite and StarDrop are similar in predicting the correct site of metabolism by CYP3A4 (78% vs 83%, respectively). StarDrop appears to do slightly better in predicting the correct site of metabolism by CYP2C9 and CYP2D6 metabolism (89% and 93%, respectively) compared to MetaSite (63% and 70%, respectively). The sites of metabolism (SOM) from 34 in-house NCEs incubated in human liver microsomes or human hepatocytes were also evaluated using two prediction software packages and the results showed comparable SOM predictions. What makes this comparison challenging is that the contribution of each isoform to the intrinsic clearance (Clint) is not known. Overall the software were comparable except for MetaSite performing better for CYP2D6 and that MetaSite has a liver model that is absent in StarDrop that predicted with 82% accuracy.