Cardiac risk assessment based on early Phase I data and PK-QTc analysis is concordant with the outcome of thorough QTc trials: an assessment based on eleven drug candidates.

Cardiac safety assessment is a key regulatory requirement for almost all new drugs. Until recently, one evaluation aspect was via a specifically designated, expensive, and resource intensive thorough QTc study, and a by-time-point analysis using an intersection-union test (IUT). ICH E14 Q&A (R3) (http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf) allows for analysis of the PK-QTc relationship using early Phase I data to assess QTc liability. In this paper, we compared the cardiac risk assessment based on the early Phase I analysis with that from a thorough QTc study across eleven drug candidate programs, and demonstrate that the conclusions are largely the same. The early Phase I analysis is based upon a linear mixed effect model with known covariance structure (Dosne et al. in Stat Med 36(24):3844-3857, 2017). The treatment effect was evaluated at the supratherapeutic Cmax as observed in the thorough QTc study using a non-parametric bootstrap analysis to generate 90% confidence intervals for the treatment effect, and implementation of the standardized methodology in R and SAS software yielded consistent results. The risk assessment based on the concentration-response analysis on the early Phase I data was concordant with that based on the standard analysis of the thorough QTc study for nine out of the eleven drug candidates. This retrospective analysis is consistent with and supportive of the conclusion of a previous prospective analysis by Darpo et al. (Clin Pharmacol Ther 97(4):326-335, 2015) to evaluate whether C-QTc analysis can detect QTc effects in a small study with healthy subjects.

Brain Imaging of Alzheimer Dementia Patients and Elderly Controls with 18F-MK-6240, a PET Tracer Targeting Neurofibrillary Tangles.

18F-MK-6240 (18F-labeled 6-(fluoro)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine) is a highly selective, subnanomolar-affinity PET tracer for imaging neurofibrillary tangles (NFTs). Plasma kinetics, brain uptake, and preliminary quantitative analysis of 18F-MK-6240 in healthy elderly (HE) subjects, subjects with clinically probable Alzheimer disease (AD), and subjects with amnestic mild cognitive impairment were characterized in a study that is, to our knowledge, the first to be performed on humans. Methods: Dynamic PET scans of up to 150 min were performed on 4 cognitively normal HE subjects, 4 AD subjects, and 2 amnestic mild cognitive impairment subjects after a bolus injection of 152-169 MBq of 18F-MK-6240 to evaluate tracer kinetics and distribution in brain. Regional SUV ratio (SUVR) and distribution volume ratio were determined using the cerebellar cortex as a reference region. Total distribution volume was assessed by compartmental modeling using radiometabolite-corrected input function in a subgroup of 6 subjects. Results:18F-MK-6240 had rapid brain uptake with a peak SUV of 3-5, followed by a uniformly quick washout from all brain regions in HE subjects; slower clearance was observed in regions commonly associated with NFT deposition in AD subjects. In AD subjects, SUVR between 60 and 90 min after injection was high (approximately 2-4) in regions associated with NFT deposition, whereas in HE subjects, SUVR was approximately 1 across all brain regions, suggesting high tracer selectivity for binding NFTs in vivo. 18F-MK-6240 total distribution volume was approximately 2- to 3-fold higher in neocortical and medial temporal brain regions of AD subjects than in HE subjects and stabilized by 60 min in both groups. Distribution volume ratio estimated by the Logan reference tissue model or compartmental modeling correlated well (R2 > 0.9) to SUVR from 60 to 90 min for AD subjects. Conclusion:18F-MK-6240 exhibited favorable kinetics and high binding levels to brain regions with a plausible pattern for NFT deposition in AD subjects. In comparison, negligible tracer binding was observed in HE subjects. This pilot study suggests that simplified ratio methods such as SUVR can be used to quantify NFT binding. These results support further clinical development of 18F-MK-6240 for potential application in longitudinal studies.

Clinical Evaluation of MK-2640: An Insulin Analog With Glucose-Responsive Properties.

The goal of this investigation was to examine clinical translation of glucose responsiveness of MK-2640, which is a novel insulin saccharide conjugate that can bind the insulin receptor or mannose receptor C type 1 (MRC1), the latter dependent upon glucose concentration. In a rising dose study in 36 healthy adults under euglycemic clamp conditions, rising exposures revealed saturation of MK-2640 clearance, likely due to saturation of clearance by MRC1. Potency of MK-2640 was ~25-fold reduced relative to regular human insulin. In a randomized, 2-period crossover trial in 16 subjects with type 1 diabetes mellitus to evaluate glucose-responsiveness of i.v. administered MK-2640, we were unable to demonstrate a glucose-dependent change in MK-2640 clearance, although a significant glucose-dependent augmentation of glucose infusion rate was observed. These pharmacokinetic (PK) and pharmacodynamic (PD) data provide crucial insights into next steps for developing an insulin saccharide conjugate as a clinically effective glucose-responsive insulin analog.

Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis.

Given the recognition that disease-modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient-level longitudinal data of 672 subjects with early-stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed-effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was -3.16 (90% confidence interval [CI] = -0.96 to -5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.

Enabling robust assessment of QTc prolongation in early phase clinical trials.

Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a "thorough QTc" (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration-QTc (conc-QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study. In December 2015, ICH released an "E14 Q&As (R3)" document supporting the use of conc-QTc modeling for regulatory decisions. In this article, we propose a simple improvement of two popular conc-QTc assessment methods for typical first-in-human crossover-like single ascending dose clinical pharmacology trials. The improvement is achieved, in part, by leveraging routinely encountered (and expected) intrasubject correlation patterns encountered in such trials. A real example involving a single ascending dose and corresponding TQT trial, along with results from a simulation study, illustrate the strong performance of the proposed method. The improved conc-QTc assessment will further enable highly reliable go/no-go decisions in early phase clinical development and deliver results that support subsequent TQT study waivers by regulators.

A semi-mechanistic model for the effects of a novel glucagon receptor antagonist on glucagon and the interaction between glucose, glucagon, and insulin applied to adaptive phase II design.

A potent novel compound (MK-3577) was developed for the treatment of type 2 diabetes mellitus (T2DM) through blocking the glucagon receptor. A semi-mechanistic model was developed to describe the drug effect on glucagon and the interaction between glucagon, insulin, and glucose in healthy subjects (N = 36) during a glucagon challenge study in which glucagon, octreotide (Sandostatin), and basal insulin were infused for 2 h starting from 3, 12, or 24 h postdose of a single 0-900 mg MK-3577 administration. The drug effect was modeled by using an inhibitory E max model (I max = 0.96 and IC50 = 13.9 nM) to reduce the ability of glucagon to increase the glucose production rate (GPROD). In addition, an E max model (E max = 0.79 and EC50 = 575 nM) to increase glucagon secretion by the drug was used to account for the increased glucagon concentrations prechallenge (via compensatory feedback). The model adequately captured the observed profiles of glucagon, glucose, and insulin pre- and postchallenge. The model was then adapted for the T2DM patient population. A linear model to correlate fasting plasma glucose (FPG) to weighted mean glucose (WMG) was developed and provided robust predictions to assist with the dose adjustment for the interim analysis of a phase IIa study.

A Bayesian approach for PK/PD modeling with PD data below limit of quantification.

A Bayesian approach to handling below limit of quantification (BLOQ) pharmacodynamic (PD) data in pharmacokinetic/pharmacodynamic (PK/PD) modeling is described. The inhibitory sigmoid Emax model is used to illustrate the implementation of the Bayesian approach for modeling BLOQ PD data. Details on how to implement this Bayesian approach via the Markov-chain Monte Carlo (MCMC) technique using WinBUGS software are presented. A simulation study was conducted to evaluate the performance of the proposed Bayesian approach and to compare the Bayesian approach with two other ad hoc approaches: replacing BLOQ data with ½LOQ, and ignoring the BLOQ data. The simulation study indicates that the proposed Bayesian approach performs better than the other two ad hoc approaches and should be considered in practice as a complementary tool for BLOQ data analysis. A case study with real PK/PD data is provided to illustrate the application of the Bayesian approach of handling BLOQ PD data in PK/PD modeling.

Clinical trials simulation: a statistical approach.

A generic template for clinical trials simulations that are typically required by statisticians is developed. Realistic clinical trials data sets are created using a unifying model that allows general correlation structures for endpoint*timepoint data and nonnormal distributions (including time-to-event), and computationally efficient algorithms are presented. The model allows for patient dropout and noncompliance. A grid-enabled SAS-based system has been developed to implement this model; details are presented summarizing the system development. An example illustrating use of the system is given.

Investigation of protein refolding using a fractional factorial screen: a study of reagent effects and interactions.

A recurring obstacle for structural genomics is the expression of insoluble, aggregated proteins. In these cases, the use of alternative salvage strategies, like in vitro refolding, is hindered by the lack of a universal refolding method. To overcome this obstacle, fractional factorial screens have been introduced as a systematic and rapid method to identify refolding conditions. However, methodical analyses of the effectiveness of refolding reagents on large sets of proteins remain limited. In this study, we address this void by designing a fractional factorial screen to rapidly explore the effect of 14 different reagents on the refolding of 33 structurally and functionally diverse proteins. The refolding data was analyzed using statistical methods to determine the effect of each refolding additive. The screen has been miniaturized for automation resulting in reduced protein requirements and increased throughput. Our results show that the choice of pH and reducing agent had the largest impact on protein refolding. Bis-mercaptoacetamide cyclohexane (BMC) and tris (2-carboxyethylphosphine) (TCEP) were superior reductants when compared to others in the screen. BMC was particularly effective in refolding disulfide-containing proteins, while TCEP was better for nondisulfide-containing proteins. From the screen, we successfully identified a positive synergistic interaction between nondetergent sulfobetaine 201 (NDSB 201) and BMC on Cdc25A refolding. The soluble protein resulting from this interaction crystallized and yielded a 2.2 Angstroms structure. Our method, which combines a fractional factorial screen with statistical analysis of the data, provides a powerful approach for the identification of optimal refolding reagents in a general refolding screen.