Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidance from Regulatory Agencies: Guidelines on Model Fitting and Recommendations on Time Course for In Vitro Cytochrome P450 Induction Studies Including Impact on Drug Interaction Risk Assessment.

Translational and ADME Sciences Leadership Group Induction Working Group (IWG) presents an analysis on the time course for cytochrome P450 induction in primary human hepatocytes. Induction of CYP1A2, CYP2B6, and CYP3A4 was evaluated by seven IWG laboratories after incubation with prototypical inducers (omeprazole, phenobarbital, rifampicin, or efavirenz) for 6-72 hours. The effect of incubation duration and model-fitting approaches on induction parameters (Emax and EC50) and drug-drug interaction (DDI) risk assessment was determined. Despite variability in induction response across hepatocyte donors, the following recommendations are proposed: 1) 48 hours should be the primary time point for in vitro assessment of induction based on mRNA level or activity, with no further benefit from 72 hours; 2) when using mRNA, 24-hour incubations provide reliable assessment of induction and DDI risk; 3) if validated using prototypical inducers (>10-fold induction), 12-hour incubations may provide an estimate of induction potential, including characterization as negative if <2-fold induction of mRNA and no concentration dependence; 4) atypical dose-response ("bell-shaped") curves can be addressed by removing points outside an established confidence interval and %CV; 5) when maximum fold induction is well defined, the choice of nonlinear regression model has limited impact on estimated induction parameters; 6) when the maximum fold induction is not well defined, conservative DDI risk assessment can be obtained using sigmoidal three-parameter fit or constraining logistic three- or four-parameter fits to the maximum observed fold induction; 7) preliminary data suggest initial slope of the fold induction curve can be used to estimate Emax/EC50 and for induction risk assessment. SIGNIFICANCE STATEMENT: Regulatory agencies provide inconsistent guidance on the optimum length of time to evaluate cytochrome P450 induction in human hepatocytes, with EMA recommending 72 hours and FDA suggesting 48-72 hours. The Induction Working Group analyzed a large data set generated by seven member companies and determined that induction response and drug-drug risk assessment determined after 48-hour incubations were representative of 72-hour incubations. Additional recommendations are provided on model-fitting techniques for induction parameter estimation and addressing atypical concentration-response curves.

Myeloperoxidase inhibition in mice alters atherosclerotic lesion composition.

Myeloperoxidase (MPO) is a highly abundant protein within the neutrophil that is associated with lipoprotein oxidation, and increased plasma MPO levels are correlated with poor prognosis after myocardial infarct. Thus, MPO inhibitors have been developed for the treatment of heart failure and acute coronary syndrome in humans. 2-(6-(5-Chloro-2-methoxyphenyl)-4-oxo-2-thioxo-3,4-dihydropyrimidin-1(2H)-yl)acetamide PF-06282999 is a recently described selective small molecule mechanism-based inactivator of MPO. Here, utilizing PF-06282999, we investigated the role of MPO to regulate atherosclerotic lesion formation and composition in the Ldlr-/- mouse model of atherosclerosis. Though MPO inhibition did not affect lesion area in Ldlr-/- mice fed a Western diet, reduced necrotic core area was observed in aortic root sections after MPO inhibitor treatment. MPO inhibition did not alter macrophage content in and leukocyte homing to atherosclerotic plaques. To assess non-invasive monitoring of plaque inflammation, [18F]-Fluoro-deoxy-glucose (FDG) was administered to Ldlr-/- mice with established atherosclerosis that had been treated with clinically relevant doses of PF-06282999, and reduced FDG signal was observed in animals treated with a dose of PF-06282999 that corresponded with reduced necrotic core area. These data suggest that MPO inhibition does not alter atherosclerotic plaque area or leukocyte homing, but rather alters the inflammatory tone of atherosclerotic lesions; thus, MPO inhibition could have utility to promote atherosclerotic lesion stabilization and prevent atherosclerotic plaque rupture.

Feasibility of Singlet Analysis for Ligand Binding Assays: a Retrospective Examination of Data Generated Using the Gyrolab Platform.

There are many sources of analytical variability in ligand binding assays (LBA). One strategy to reduce variability has been duplicate analyses. With recent advances in LBA technologies, it is conceivable that singlet analysis is possible. We retrospectively evaluated singlet analysis using Gyrolab data. Relative precision of duplicates compared to singlets was evaluated using 60 datasets from toxicokinetic (TK) or pharmacokinetic (PK) studies which contained over 23,000 replicate pairs composed of standards, quality control (QC), and animal samples measured with 23 different bioanalytical assays. The comparison was first done with standard curve and QCs followed by PK parameters (i.e., Cmax and AUC). Statistical analyses were performed on combined duplicate versus singlets using a concordance correlation coefficient (CCC), a measurement used to assess agreement. Variance component analyses were conducted on PK estimates to assess the relative analytical and biological variability. Overall, 97.5% of replicate pairs had a %CV of <11% and 50% of the results had a %CV of ≤1.38%. There was no observable bias in concentration comparing the first replicate with the second (CCC of 0.99746 and accuracy value of 1). The comparison of AUC and Cmax showed no observable difference between singlet and duplicate (CCC for AUC and Cmax >0.99999). Analysis of variance indicated an AUC inter-subject variability 35.3-fold greater than replicate variability and 8.5-fold greater for Cmax. Running replicates from the same sample will not significantly reduce variation or change PK parameters. These analyses indicated the majority of variance was inter-subject and supported the use of a singlet strategy.

Plasma Protein Binding of Challenging Compounds.

Accurately determining fraction unbound (fu ) with currently available methods has been challenging for certain compounds. Inaccurate fu values can lead to the misinterpretation of important attributes of a drug candidate. Our analysis of over 2000 Pfizer drug discovery compounds showed no systematic bias in low or high fu precision using the equilibrium dialysis method. However, the accuracy of the plasma protein binding (PPB) estimate for highly bound compounds may be poor, should equilibrium not be fully achieved in the equilibrium dialysis assay. Here, a dilution method and a presaturation method were applied to accelerate equilibration for a set of challenging compounds. These improved methods demonstrate the ability to provide an accurate measurement of PPB for highly bound compounds with fu values much less than 1%. Therefore, we recommend that the actual experimental fu value be used for the prediction of drug-drug interaction potential and for the characterization of important drug candidate properties. Our recommendation calls into question the need for current regulatory guidelines that restrict the reporting of fu values below 1%.

A "middle-out" approach to human pharmacokinetic predictions for OATP substrates using physiologically-based pharmacokinetic modeling.

Physiologically based pharmacokinetic (PBPK) models provide a framework useful for generating credible human pharmacokinetic predictions from data available at the earliest, preclinical stages of pharmaceutical research. With this approach, the pharmacokinetic implications of in vitro data are contextualized via scaling according to independent physiological information. However, in many cases these models also require model-based estimation of additional empirical scaling factors (SFs) in order to accurately recapitulate known human pharmacokinetic behavior. While this practice clearly improves data characterization, the introduction of empirically derived SFs may belie the extrapolative power commonly attributed to PBPK. This is particularly true when such SFs are compound dependent and/or when there are issues with regard to identifiability. As such, when empirically-derived SFs are necessary, a critical evaluation of parameter estimation and model structure are prudent. In this study, we applied a global optimization method to support model-based estimation of a single set of empirical SFs from intravenous clinical data on seven OATP substrates within the context of a previously published PBPK model as well as a revised PBPK model. The revised model with experimentally measured unbound fraction in liver, permeability between liver compartments, and permeability limited distribution to selected tissues improved data characterization. We utilized large-sample approximation and resampling approaches to estimate confidence intervals for the revised model in support of forward predictions that reflect the derived uncertainty. This work illustrates an objective approach to estimating empirically-derived SFs, systematically refining PBPK model performance and conveying the associated confidence in subsequent forward predictions.

An inferential framework for biological network hypothesis tests.

BACKGROUND: Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem. RESULTS: We outline an inferential method for performing one- and two-sample hypothesis tests where the sampling unit is a network and the hypotheses are stated via network model(s). We propose a dissimilarity measure that incorporates nearby neighbour information to contrast one or more networks in a statistical test. We demonstrate and explore the utility of our approach with both simulated and microarray data; random graphs and weighted (partial) correlation networks are used to form network models. Using both a well-known diabetes dataset and an ovarian cancer dataset, the methods outlined here could better elucidate co-regulation changes for one or more pathways between two clinically relevant phenotypes. CONCLUSIONS: Formal hypothesis tests for gene- or protein-based networks are a logical progression from existing gene-based and gene-set tests for differential expression. Commensurate with the growing appreciation and development of systems biology, the dissimilarity-based testing methods presented here may allow us to improve our understanding of pathways and other complex regulatory systems. The benefit of our method was illustrated under select scenarios.

RCMAT: a regularized covariance matrix approach to testing gene sets.

BACKGROUND: Gene sets are widely used to interpret genome-scale data. Analysis techniques that make better use of the correlation structure of microarray data while addressing practical "n