Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases.

A substantial body of research is focused to improve the understanding of the relationship between genotypes and phenotypes. Genotype-phenotype studies have shown promise in improving disease diagnosis in humans and identification of specific clinical phenotypes may be helpful in developing more effective therapeutic and diagnostic strategies. To expand on the existing paradigm of evaluating genotypes and phenotypes, we present an investigation of the correlation between biological processes as represented by genomic information and phenotypes in human disease. We focus on monogenic diseases and link biological process and phenotype utilizing information from the Online Mendelian Inheritance in Man, the Gene Ontology, and the Human Phenotype Ontology comprehensive genomic, phenotypic, and disease information resources. Our study uncovers 4661 statistically significant associations and identifies novel correlations between biological processes and phenotypes. We find new relationships between unique phenotype-genotype pairs related to cardiovascular diseases and hypertelorism, which suggests that differences between certain phenotype-genotype association may be the key to the divergence of corresponding phenotypes. Although the application of correlating genotype, phenotype, and biological processes may help to guide diagnosis and treatment of diseases, further investigation and more specific gene ontology descriptions are still required to elucidate mechanisms of action.

Identification of Drug-Induced Multichannel Block and Proarrhythmic Risk in Humans Using Continuous T Vector Velocity Effect Profiles Derived From Surface Electrocardiograms.

We present continuous T vector velocity (TVV) effect profiles as a new method for identifying drug effects on cardiac ventricular repolarization. TVV measures the temporal change in the myocardial action potential distribution during repolarization. The T vector dynamics were measured as the time required to reach p percent of the total T vector trajectory length, denoted as Tr(p), with p in {1, …, 100%}. The Tr(p) values were individually corrected for heart rate at each trajectory length percentage p. Drug effects were measured by evaluating the placebo corrected changes from baseline of Tr(p)c jointly for all p using functional mixed effects models. The p-dependent model parameters were implemented as cubic splines, providing continuous drug effect profiles along the entire ventricular repolarization process. The effect profile distributions were approximated by bootstrap simulations. We applied this TVV-based analysis approach to ECGs available from three published studies that were conducted in the CiPA context. These studies assessed the effect of 10 drugs and drug combinations with different ion channel blocking properties on myocardial repolarization in a total of 104 healthy volunteers. TVV analysis revealed that blockade of outward potassium currents alone presents an effect profile signature of continuous accumulation of delay throughout the entire repolarization interval. In contrast, block of inward sodium or calcium currents involves acceleration, which accumulates during early repolarization. The balance of blocking inward versus outward currents was reflected in the percentage pzero of the T vector trajectory length where accelerated repolarization transitioned to delayed repolarization. Binary classification using a threshold pzero = 43% separated predominant hERG channel blocking drugs with potentially higher proarrhythmic risk (moxifloxacin, dofetilide, quinidine, chloroquine) from multichannel blocking drugs with low proarrhythmic risk (ranolazine, verapamil, lopinavir/ritonavir) with sensitivity 0.99 and specificity 0.97. The TVV-based effect profile provides a detailed view of drug effects throughout the entire ventricular repolarization interval. It enables the evaluation of drug-induced blocks of multiple cardiac repolarization currents from clinical ECGs. The proposed pzero parameter enhances identification of the proarrhythmic risk of a drug beyond QT prolongation, and therefore constitutes an important tool for cardiac arrhythmia risk assessment.

T vector velocity: A new ECG biomarker for identifying drug effects on cardiac ventricular repolarization.

BACKGROUND: We present a new family of ECG biomarkers for assessing drug effects on ventricular repolarization. We show that drugs blocking inward (depolarizing) ion currents cause a relative increase of the T vector velocity (TVV) and accelerate repolarization, while drugs blocking outward ion currents cause a relative decrease of the TVV and delay repolarization. The results suggest a link between the TVV and the instantaneous change of the cellular action potentials that may contribute to bridge the gap between the surface ECG and myocardial cellular processes. METHODS: We measure TVV as the time required to reach X% of the total Trajectory length of the T vector loop, denoted as TrX. Applied to data from two FDA funded studies (22+22 subjects, 5232+4208 ECGs) which target ECG effects of various ion-channel blocking drugs, the TrX effect profiles indicate increasingly delayed electrical activity over the entire repolarization process for drugs solely reducing outward potassium current (dofetilide, moxifloxacin). For drugs eliciting block of the inward sodium or calcium currents (mexiletine, lidocaine), the TrX effect profiles were consistent with accelerated electrical activity in the initial repolarization phase. For multichannel blocking drugs (ranolazine) or drug combinations blocking multiple ion currents (dofetilide + mexiletine, dofetilide + lidocaine), the overall TrX effect profiles indicate a superposition of the individual TrX effect profiles. RESULTS: The parameter Tr40c differentiates pure potassium channel blocking drugs from multichannel blocking drugs with an area under the ROC curve (AUC) of 0.90, CI = [0.88 to 0.92]. This is significantly better than the performance of J-Tpeakc (0.81, CI = [0.78 to 0.84]) identified as the best parameter in the second FDA study. Combining the ten parameters Tr10c to Tr100c in a logistic regression model further improved the AUC to 0.94, CI = [0.92 to 0.96]. CONCLUSIONS: TVV analysis substantially improves assessment of drug effects on cardiac repolarization, providing a plausible and improved mechanistic link between drug effects on ionic currents and overall ventricular repolarization reflected in the body surface ECG. TVV contributes to an enhanced appraisal of the proarrhythmic risk of drugs beyond QTc prolongation and J-Tpeakc.

Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering.

A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q-T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de pointes). Such prolongation is also a widely accepted cardiac safety biomarker in drug development. Current preclinical drug-safety assays include patch clamp analysis to evaluate drug-related blockade of cardiac repolarizing ion currents. Recently reported patch clamp assay results have shown predictive sensitivities and specificities in the ranges of 64%-82% and 75%-88%, respectively. In this project, we use a support vector machine classifier to find mean sensitivities and specificities of 85% and 90%, respectively, across 77 drug subclassifications. Clustering by gene expression profile similarities shows that drugs known to prolong the QT interval do not always form distinct groups, but the number of groups is limited. The most common biological network links associated with these groups involve genes linked with fatty acid metabolism, G proteins, intracellular glutathione, immune responses, apoptosis, mitochondrial function, electron transport, and mitogen-activated protein kinases. These results suggest that machine-learning analysis of gene expression and clustering may augment cardiac safety predictions for improving drug-safety assessments.

Effect of veliparib (ABT-888) on cardiac repolarization in patients with advanced solid tumors: a randomized, placebo-controlled crossover study.

PURPOSE: Veliparib (ABT-888) is an orally bioavailable potent inhibitor of poly(ADP-ribose) polymerase (PARP)-1 and PARP-2. This phase 1 study evaluated the effect of veliparib on corrected QT interval using Fridericia's formula (QTcF). METHODS: Eligible patients with advanced solid tumors received single-dose oral veliparib (200 mg or 400 mg) or placebo in a 6-sequence, 3-period crossover design. The primary endpoint was the difference in the mean baseline-adjusted QTcF between 400 mg veliparib and placebo (∆∆QTcF) at six post-dose time points. Absence of clinically relevant QTcF effect was shown if the 95 % upper confidence bound (UCB) for the mean ∆∆QTcF was <10 ms for all time points. An exposure-response analysis was also performed. RESULTS: Forty-seven patients were enrolled. Maximum mean ∆∆QTcF of veliparib 400 mg was 6.4 ms, with a 95 % UCB of 8.9 ms; for veliparib 200 mg, the maximum mean ∆∆QTcF was 3.6 ms, with a 95 % UCB of 6.1 ms. No patient had a QTcF value >480 ms or change from baseline in QTcF interval >30 ms. Treatment-emergent adverse events (TEAEs) were experienced by 36.2, 48.9, and 47.8 % of patients while receiving veliparib 200 mg, veliparib 400 mg, and placebo, respectively. Most common TEAEs were nausea (12.8 %) and myalgia (8.5 %) after veliparib 200 mg, nausea (8.5 %) and vomiting (8.5 %) after veliparib 400 mg, and nausea (6.5 %) after placebo. CONCLUSIONS: Single-dose veliparib (200 mg or 400 mg) did not result in clinically significant QTc prolongation and was well tolerated in patients with advanced solid tumors.